Mathematical models of malaria  a review
 Sandip Mandal^{1},
 Ram Rup Sarkar^{1} and
 Somdatta Sinha^{1}Email author
DOI: 10.1186/1475287510202
© Mandal et al; licensee BioMed Central Ltd. 2011
Received: 11 September 2010
Accepted: 21 July 2011
Published: 21 July 2011
Abstract
Mathematical models have been used to provide an explicit framework for understanding malaria transmission dynamics in human population for over 100 years. With the disease still thriving and threatening to be a major source of death and disability due to changed environmental and socioeconomic conditions, it is necessary to make a critical assessment of the existing models, and study their evolution and efficacy in describing the hostparasite biology. In this article, starting from the basic Ross model, the key mathematical models and their underlying features, based on their specific contributions in the understanding of spread and transmission of malaria have been discussed. The first aim of this article is to develop, starting from the basic models, a hierarchical structure of a range of deterministic models of different levels of complexity. The second objective is to elaborate, using some of the representative mathematical models, the evolution of modelling strategies to describe malaria incidence by including the critical features of hostvectorparasite interactions. Emphasis is more on the evolution of the deterministic differential equation based epidemiological compartment models with a brief discussion on data based statistical models. In this comprehensive survey, the approach has been to summarize the modelling activity in this area so that it helps reach a wider range of researchers working on epidemiology, transmission, and other aspects of malaria. This may facilitate the mathematicians to further develop suitable models in this direction relevant to the present scenario, and help the biologists and public health personnel to adopt better understanding of the modelling strategies to control the disease
Background
Malaria is an ancient disease having a huge social, economic, and health burden. It is predominantly present in the tropical countries. Even though the disease has been investigated for hundreds of years, it still remains a major public health problem with 109 countries declared as endemic to the disease in 2008. There were 243 million malaria cases reported, and nearly a million deaths  primarily of children under 5 years [1]. With no effective vaccine in sight and many of the older antimalarial drugs losing effectiveness due to the parasite evolving drug resistance, prevention (using bed nets) is still the only advisory given to afflicted persons. Malaria has also gained prominence in recent times since climate change or global warming is predicted to have unexpected effects on its incidence. Both increase and fluctuation in temperature affects the vector and parasite life cycle. This can cause reduced prevalence of the disease in some areas, while it may increase in others. Thus climate change can affect malaria prevalence pattern by moving away from lower latitudes to regions where populations have not developed immunity to the disease [2–8].
Malaria is caused by the protozoan parasites of genus Plasmodium. In humans it is caused by Plasmodium falciparum, Plasmodium malariae, Plasmodium ovale, and Plasmodium vivax. Of these, P. falciparum is the most common cause of infection in Africa and South East Asia, and is responsible for ~80% of all malaria cases and ~90% of deaths [1]. In India, P. vivax, has been the primary pathogen responsible for malaria, even though P. falciparum cases are on the rise in recent times [9]. The parasite requires two hosts to complete its life cycle  the vector female Anopheles mosquito and human. The bites/bloodmeals of infected mosquitoes are the mode of transmission of the parasite between the human hosts. Grassi and Ross discovered the mosquito's role in the parasite life cycle and transmission in 1897 [1], and the genomes of Anopheles mosquito and P. falciparum were sequenced in 2002 [10, 11]. During the interim 105 years, much scientific research was undertaken and progress made in the understanding of the hostparasitevector interactions and their biology. However, the complexities in the life cycle of the parasite, highly complex environmental and social interactions, evolutionary pressure of drugs and control measures contributing to drug resistance of parasite, unforeseen effects of climate change, and migration of population between endemic and non endemic areas continued to contribute to the huge burden of morbidity and mortality accompanying the disease. These have also thrown up new challenges to researchers and public health professionals.
Among all areas in Biology, researchers in infectious disease were one of the foremost to realize the important role of mathematics and mathematical models in providing an explicit framework for understanding the disease transmission dynamics within and between hosts and parasites. In a mathematical expression or a model, several known clinical and biological information are included in a simplified form by selecting features that seem to be important to the question being investigated in disease progression and dynamics. Therefore, a model is an "approximation" of the complex reality, and its structure depends upon the processes being studied and aimed for extrapolation. Based on the questions being asked, these studies can help fit empirical observations, and can be applied to make theoretical predictions on lesser known or unknown situations. For example, mathematical models have been widely used by epidemiologists as tools to predict the occurrence of epidemics of infectious diseases, and also as a tool for guiding research for eradication of malaria at the present time [12, 13].
Malaria is one of the oldest diseases studied for a long time from all angles, and vast literature exists describing a host of modelling approaches. Different approaches are helpful in guiding different stages of the disease through synthesizing available information and extrapolating it. It is felt that a combination of different approaches, rather than a single type of modelling, may have long term usefulness in eradication and control [13]. In the recent years, global eradication and control efforts [14, 15] have led to a surge of activities leading to many studies and publications. It is a formidable task to review all types of models in one article. In this article a historical path has been considered, and an attempt is made to take into account some of those mathematical models, which are primarily focused on the transmission dynamics of the infection in the host and vector populations, using the epidemiological compartment modelling approach [16, 17]. The modelling methodology is predominantly deterministic and differential equation based.
This in no way undermines the importance of other models that are concerned with the "within host" biology, or population genetic models that have an increasing impact in eradication and control. To study the infection phenomena inside the individual host, "within host" models consider the interaction of the parasite with the immune cells in an individual host [18–21]. Population genetic models study evolution and spread of the parasite in a complex landscape of varying host immunity, host death, drugs, and mosquito availability [22, 23]. These are connected to the parasitological status of a population, which is related to the different classes in the epidemiological compartment models. As mentioned earlier, only few recent papers are referred on these topics, and interested readers may get further leads from them. Different modelling methodologies have also been adopted in addition to differential equationbased models. Few examples are, individualbased models [24], habitatbased models [25], integrated models [26, 27], and others [17, 28–34]. In spite of the wide range of these models and methodologies, the major modelling approach still remains the transmission of infection through the epidemiological compartments of human and vector populations. Further, with the recent concern with climate change [5, 6], the importance of the power of prediction of mathematical models in understanding the infectious disease transmission, highlights the requirement of a consolidated review on this modelling strategy and evolution of the models employed till date.
Glossary of different important terms
Latent period (τ):  The period from the point of infection to the beginning of the state of infectiousness is known as Latent period during which the infected individuals stay in the exposed (E) class. 
Incubation period:  The period from the point of infection to the appearance of symptoms of disease is known as the Incubation period. 
Asymptomatic:  In some infections, symptoms do not appear in the individual in spite of being a carrier for a disease and this is called Asymptomatic Infection. The appearance of symptoms is important for case diagnosis and treatment. Sometimes asymptomatic infections are also called subclinical infections. 
Vectorial capacity:  All the information (vector density relative to host, biting rate, life expectancy etc.) about the vector populations is incorporated through vectorial capacity, which is defined as the number of potentially infective contacts an individual person makes, through the vector population, per unit time. 
Entomological Inoculation Rate (EIR):  Rate of infectious bites per person is termed as Entomological inoculation rate. 
Force of infection:  Per capita rate of acquisition of infection by infectious bites is called force of infection. 
Clinical immunity:  The immunity, which reduces the probability of clinical disease, is called Clinical immunity. 
Antiparasite immunity:  The immunity, which is responsible for clearance of parasite is called Antiparasite immunity. 
Effectiveness of treatment (∈):  The ratio of the duration of infection for the untreated and treated sensitive parasites. 
Cost of resistance (Γ):  The reduction of a resistant parasite's fitness relative to that of a sensitive parasite, when neither parasite is exposed to the drug. 
With all these models at hand, it is not a trivial matter to infer the crucial features of the disease, and get a coherent understanding of the development of the models from interactions among the vector, parasite and host. In this review, the emphasis is more on the evolution of different mathematical models (mainly differential equation based) of malaria with a brief discussion on stochastic models and data based statistical models. The first aim of this paper is to develop a hierarchical structure of the range of deterministic models of different levels of complexity, starting from the basic Ross Model. The second objective is to elaborate on the evolution of modelling strategies in different steps, using some of the key mathematical models that describe malaria incidence by including specific properties of hostvectorparasite interactions. To reach a wide range of researchers working on the epidemiology, transmission, and other aspects of malaria, the models have been critically analysed, so that it will be useful in understanding and classifying the numerous betweenhost models in this area. This may help mathematicians to further develop suitable models, and biologists and public health professionals to adopt better strategies for controlling the disease.
Model basics
In epidemiological compartment models of infectious diseases, transmission of infectious agents in the host population is the fundamental process to be described. When a pathogen appears in a host community, it partitions individuals in the community into categories depending on parasite density inside them and the type of infection. These categories or compartments are represented by standard notation of SEIR after the pioneering work of Kermack and McKendrik [16]. In a simple form they are as follows: the first group consists of the fraction of host population that is Susceptible (S) to infection; then comes the Exposed (E) class  the fraction of population whose individuals are infected by the pathogen, but not capable of passing on the infection to others during a latent period (Table 1). The next is I class or Infectious individuals, who give rise to more infected individuals through interaction with the Susceptibles. Finally, those individuals who recover from the infection make up the R class.
There may be variations in the compartment structure depending on the type of disease. For example, the I class of individuals may not recover at all and die; R can consist of individuals, who recover with temporary or permanent immunity, thereby further subdividing the epidemiological compartments. Using these notations, eight classes of compartmental models are possible  SI, SIS, SEI, SEIS, SIR, SIRS, SEIR and SEIRS[50]. For example, in an SEIRS model, a fraction of the susceptible (S) population gets exposed (E) to infection, a part of which then becomes infectious (I). Some from the I class recover from the disease, and become part of the R class with temporary immunity. When immunity is lost, they become susceptible to pathogen attack again, and enter the S class. The Plasmodium parasite requires both human and mosquito for its life cycle to complete, and the infection is transferred between susceptible human individuals through the bite of infected mosquitoes, which acquire infection through a blood meal from infected humans. In malaria models, therefore, these compartments have been applied to both human (host) and vector (mosquito).
Clinical markers for diagnosis
Polymerase Chain Reaction (P):  For identification of malaria parasites in blood, Polymerase Chain Reaction (PCR) is now a common and often vital technique, which amplifies a minute amount of DNA of the parasite across several orders of magnitude, generating thousands to millions of copies of a particular DNA sequence [51, 52]. Due to absence of parasite in susceptible (S) and recovered (R) classes PCR indicates negativity whereas for exposed (E) and infected classes (I) PCR shows positivity. 
Seroconversion (Sc):  To determine antibody positivity as a result of infection or immunization, the clinical technique Serology is used. The development of detectable specific antibodies to microorganisms in the blood serum (Seroconversion) is a reliable indicator for different infectious diseases including malaria [53]. Before Seroconversion, the blood test shows Seronegativity for the antibody (in susceptible and exposed classes); after Seroconversion it shows Seropositivity for the antibody (in infected and recovered classes). 
Cellular immunity (C):  Parasite infection lead to development of specific memory immune cells (Tcells and/or Bcells), which is detected positive through clinical diagnostic of cellular immunity (C) in the recovered class. 
Hierarchy of malaria models
The epidemiological compartments are kept in doublefold boxes in Figure 2. The human classes (S_{ h }, E_{ h }, I_{ h }, R_{ h } ) are in the left fold and the mosquito classes (S_{ m }, E_{ m }, I_{ m } ) in the right. In general, the human classes in malaria infection end with the susceptible (S_{ h } ) class, but the mosquito populations die of infection and hence it can only go up to the infected class (I_{ m } ). In models where there is only a singlefold box (for the human classes), the effect of vector is introduced through their vectorial capacity of infection (Table 1 for definition). The newly introduced compartment over the earlier one is shown in red. The effects of different complex factors, such as age, immunity, environment and socioeconomic, in different models or specific compartments (red) are shown by dotted arrows.
Ronald Ross in his first mathematical model of malaria used the word "pathometry" to mean "quantitative study of a disease either in the individual or in the community" [36]. Ross, through his model, showed that reduction of mosquito numbers "below a certain figure" (Transmission threshold) was sufficient to counter malaria  a concept far ahead of his time. After about 40 years, George Macdonald [40], in the 1950s, reasserted the usefulness of mathematical epidemiology based on 20 years of fieldwork. He modified Ross's model by integrating biological information of latency in the mosquito due to malaria parasite development, and implicated the survivorship of adult female mosquito as the weakest element in the malaria cycle. This provided a rationale for a massive World Health Organization (WHO)coordinated campaign, which focused on using the insecticide dichlorodiphenyltrichloroethane (DDT) that killed mosquitoes, for the elimination of malaria transmission among 500 million people in Africa [54, 55]. Latency of infection in humans was introduced by Anderson and May [12] in Macdonald's model making the additional "Exposed" class in humans.
Definition of basic reproductive number (R_{0})
Basic Reproductive Number (R_{ 0 })  The basic reproductive number, R_{ 0 }, is the average number of successful offspring that a parasite is capable of producing [12, 61]. For the compartmental models of malaria this is defined as the number of secondary cases of malaria arising from a single case in an otherwise uninfected population [40], and can be thought of as a measure of the intensity of transmission. The estimation of basic reproductive number (R_{ 0 }), from both models and data has been discussed by several researchers over the years [12, 59–61, 125]. Malaria can spread in a population only if R_{ 0 } > 1 (epidemic), but when it is maintained in a population without the need for external inputs R_{ 0 } = 1 (endemic). A disease free population is possible when R_{ 0 } < 1. These threshold conditions of R_{ 0 } may not hold for stochastic models. In that case the disease may go extinct even for R_{ 0 } > 1, depending on the magnitude of stochastic fluctuations around the endemic equilibrium state [12]. 
In the following sections, starting from the details of the basic models, the representative mathematical models in each group along with their underlying features are discussed, and their specific contributions are reviewed.
Important features and comparative analysis of mathematical models
Basic models
Basic malaria models  (a) Ross Model, (b) Macdonald Model, and (c) AndersonMay Model, with corresponding basic reproductive number (R_{0}) and parameter descriptions
Models  R _{ 0 }  Parameters and their values 

(a) Ross model [36]

 a : Man biting rate [0.010.5 day^{1}] b : Proportion of bites that produce infection in human [0.20.5] 
(b) Macdonald model [40]

 c : Proportion of bites by which one susceptible mosquito becomes infected [0.5] m : Ratio of number of female mosquitoes to that of humans [0.540] r : Average recovery rate of human [0.0050.05 day^{1}] 
(c) Anderson and May model [12]

 μ_{ 1 }: Per capita rate of human mortality [0.017 year^{1}] μ_{ 2 }: Per capita rate of mosquito mortality [0.050.5 day^{1}] τ_{ m }: Latent period of mosquito [515 days] τ_{ h }: Latent period of human [10100 day] 
Ross introduced the first deterministic differential equation model of malaria by dividing the human population into susceptible (S_{ h } ) and infected (I_{ h } ) compartments, with the infected class returning to susceptible class again leading to the SIS structure. The mosquito population also has only two compartments (S_{ m }, I_{ m } ), but they do not recover from infection due to their short life span, and thereby follow the SI structure. Time evolution of the fraction of individuals in the infected classes (I_{ h }, I_{ m } ) is studied using two differential equations  one each for the human and mosquito (Table 4). It is clear that the parameters, m, a, b, and c, that contribute to the increase of R_{ 0 } in this model, are related to mosquitoes and humans, and any change in them can significantly affect malaria transmission. Increasing mosquito mortality and reducing mosquito biting rate can reduce R_{ 0 } . The Ross model outlines the basic features of malaria transmission, and puts the main burden of transmission on mosquitospecific features, thereby paving the way for mosquitobased malaria control programmes.
The malaria parasite spends approximately 10 days inside a mosquito during its life cycle. The simple Ross model did not consider this latency period of the parasite in mosquitoes and their survival during that period. This resulted in the model predicting a rapid progress of the epidemic in human, and a higher equilibrium prevalence of infectious mosquitoes. Macdonald considered this latency period (t_{ m } ), and introduced the Exposed (E_{ m } ) class in the mosquitoes [40]. Therefore, in this model (Table 4), the mosquito population is divided into three compartments (SEI), and the model studies the time evolution of the exposed (E_{ m } ) and infected (I_{ m } ) classes in mosquito. The R_{ 0 } for this model is consequently scaled down with increasing latency period.
Predicting the effects of interventions in the basic models
The parameters mosquito density (m), biting rate (a) and mosquito mortality rate (μ_{ 2 } ) are important in regulating the fraction of human population that will enter into the exposed (E) and infected (I) classes. The most important fact for any epidemiologist or public health person is to have an idea about the relative effects of interventions in these parameters to the intensity of transmission, the measure of which is R_{ 0 } . Given the expressions of R_{ 0 } in all three models in Table 4 it is clear that the square dependence of the biting rate 'a' implies that halving the biting rate is more effective than halving the coefficients 'b' or 'c' in all three models Thus, reducing the biting rate (by using bed nets, or any other method) will be an effective method of controlling the transmission. But this is not so obvious for all parameters. For example, the relative effect of reducing the adult mosquito mortality (μ_{ 2 } ) in comparison to biting rate (a) is different in these models due to the presence of the exponential function of μ_{ 2 } . Figure 3b shows this with the Ross and AndersonMay models, where the variation of R_{ 0 } is plotted with changes in two parameters, biting rate (a) and adult mosquito mortality (μ_{ 2 } ) with other parameters as per Table 4. Due to higher disease prevalence predicted by Ross model (see Figure 3a), the R_{ 0 } surface is also higher compared to that of the AndersonMay model. The surface of R_{ 0 } = 1 shows that onset of epidemic happens at higher values of parameters in Ross model compared to AndersonMay model. These results indicate that compared to the reduction in biting rate 'a', reducing the length of life of adult mosquitoes is most effective in decreasing malaria cases, in the latter two of the three basic models. As mentioned before, these model results provided rationale for control of malaria transmission through the mosquitoes, using insecticides (DDT) and insecticideimpregnated bed nets, since they affect m, a, and μ_{ 2 } . Thus, even at this low level of complexity, these models had been successful in describing the factors that influence the transmission of the disease, which were useful in control and eradication of malaria from many countries of the world.
Complex models
Over and above the simple scenario described in the basic models, many other factors such as, host factors, demographic heterogeneity, geographic distribution of populations, rules of social interactions, climate and environmental influences, and the ecology of the area play important roles in the development of malaria in space and time. Agespecific host immunity, parasite diversity, DDT and drug resistance dynamics, vector population dynamics, effect of global warming are also interacting factors and variables that influence disease dynamics at different scales. There has not been A MODEL that has been able to incorporate all factors and variables because of the overwhelming complexity of the system. Also, a model's utility may not always lie in its mathematical analysis or incorporating finer details. The ability to base it on relevant details and ask specific questions that can be tested, are the hallmark of useful models. Along with fitting the past data and predicting the future, it should also be able to point to areas where data needs to be generated in order to increase our conceptual grasp. Such improvements in modelling generally occur in multiple steps, one leading to the other, as more information become available. The next section elaborates on some representative nextgeneration mathematical models that evolved from the abovementioned basic models, and includes the increasing complexities of hostvectorparasite interactions. Specifically, the factors considered here are  (i) Age and immunity, (ii) HostPathogen variability and resistant Strains, (iii) Environmental factors, (iv) Social and economic factors, and (v) Migration and visitation.
Age and immunity
Malaria burden differs depending on age and gender in humans. In African children, most malaria deaths occur under the age of 5years. As a result of continuous exposure and the ability to develop a degree of immunity to the disease, older Africans have reduced risk. Outside Africa, where continuous exposure does not occur, the disease burden extends into adulthood [1]. Age and immunity, therefore, are known to be important interrelated factors for transmission of malaria in a population. The importance of incorporation of immunity in malaria models is aptly described by Koella [56]  "Incorporating immunity into malaria models is important for two reasons. First, the neglect of immunity leads to unrealistic predictions. Incorporating immunity can help to make models more realistic. Secondly, modelling immunity, and in particular the effect of vaccines, can help to predict the outcome of vaccination programmes". A number of epidemiological studies [41, 43, 44, 57, 62] have focused on this important aspect by including immunity and age structure of the human community in the models. In this scenario, the infection moves differentially within different age groups based on their immune status, and also with time.
where, N(α) denotes the population density of human at age α, and is the mosquito density. Other variables and parameters are as described in Table 4. In this scenario, the infection moves differentially within different age groups and also with time. With the inclusion of a simple force of infection (Table 1), which represents the per capita rate of acquisition of infection based on N, , 'a' and 'b', this simple model can improve upon the basic models to include the age dependence of infection in human community. But the dependence, as predicted by this model, did not match well with observed trend in prevalence with age [63], and it was clear that the interaction of age and immunity needs to be modelled more explicitly.
Immunity can be included in a model in two ways  by considering a separate Immune class (R_{ h } ) in humans, and by incorporating an Immunity function in existing models. Some models (Dietz et al[57], Aron [43], Ngwa and Shu [64], Ngwa [65], Chitnis et al[32, 66], Yang [67, 68]) have introduced a separate immune class in their models, whereas, some others (Fillipe et al[44] and [69–72]) have used complex immunity functions in their model. Assuming that the malaria immunity is not permanent, Dietz et al[57] first proposed a model considering seven compartments of human. The effect of mosquito was introduced through vectorial capacity. In this model a person may either recover from the infected class (I_{ h } ) and directly return to the susceptible class (S_{ h } ), or become reinfected through a temporary immune class (R_{ h } ). The model has shown a good fit to the data obtained from northern Nigeria. The changes on each compartment in this model were presented using difference equations. The differential equation based models that incorporate immune classes are discussed below.
Immune class
Solution of equation 2 shows how the prevalence of infection varies with the age of human. Figure 4b shows the prevalence of infection (I_{ h } ) with age at three different forces of infection (h). At higher infection (h = 4), I_{ h } rises rapidly with age in young infants and children, attains a peak, and then declines in the older children to reach a low level in adults. Prevalence in adults decreases due to the increase in immunity. For low h (h = 0.04), this dependence on age is negligible. This model predicts that the prevalence rises quickly in early childhood and declines slowly into adulthood in highly endemic areas, due to slow acquisition of immunity with age/time. Interestingly, the prevalence among adults is highest when h is in an intermediate value. The adult crossover of the ageprevalence curve with increasing h resembles the pattern of acute infection described by Boyd in tropical Africa [63].
Inclusion of the "Recovered" class with immunity of the host has been the source of many later models that considered other variations in hostpathogen interactions [12, 56]. A few models are mentioned below, which along with introducing the "Recovered" class in humans in the AndersonMay (AM) model, also differ in some of the critical assumptions from the models discussed so far. One of the features that has been consistently followed in all the models discussed above is the constancy of population size. Mortality and migrations are major factors in changing the population size in an area and the inclusion of varying population size in the model makes them more realistic.
Ngwa and Shu proposed an immunity model in which disease related death rate is considered to be significantly high, and the total population is not constant (shown inside the dashed box in Figure 2). The NgwaShu model [64] model consists of four compartments in humans  Susceptible (S_{ h } ), Exposed (E_{ h } ), Infected (I_{ h } ) and Immune (R_{ h } )  and three compartments in mosquitoes  Susceptible (S_{ m } ), Exposed (E_{ m } ), and Infected (I_{ m } ) (see Additional file 1 Table S1). Mathematical analysis of the model shows that the Basic Reproductive Number, R_{ 0 } , can describe the malaria transmission dynamics of the disease, where a globally stable diseasefree state exists if R_{ 0 } < 1, while for R_{ 0 } > 1, the endemic equilibrium becomes globally stable. This model explicitly shows the role of inclusion of demographic effects (net population growth) in predicting the number of fatalities that may arise as a result of the disease. In a similar theme, Chitins et al[32, 66] included constant immigration of susceptible human population, (see Additional file 1 Table S2). Considering immigration of people and excluding direct human recovery from the infectious to susceptible class (as is considered in other models here), they showed that the population approaches the locally asymptotically stable endemic equilibrium point, or stable diseasefree equilibrium point, depending on the initial size of the susceptible class.
Immunity can be described as a continuum of different levels of protection rather than a single class, as antimalarial immunity develops slowly among people exposed to continuous and intense malaria transmission. Yang [67] divided the immune class (R_{ h } ) in human population into immune (R_{ h1 } ), partially immune (R_{ h2 } ) and nonimmune but with immunologic memory (R_{ h3 } ), with each class having differential immunity (see Additional file 1 Table S3). The mathematical analysis of Yang model shows that the effects of these three types of immune responses lead to delay in the reappearance of the individuals, who already had experienced malaria, to the susceptible population. Hence the community under high threat of malaria (high R_{ 0 } ) shows low prevalence of individuals with asexual bloodstage infection and without infectious gametocytes, whereas, the same community is relatively free of severe infection due to the increase in immunity by reinfection.
Immunity functions
Due to lack of confirmed markers of immunological protection, different processes that determine the immunity acquisition to clinical disease and to asymptomatic carriage of malaria parasites are poorly understood. The models discussed in the earlier section consider the immune individuals as a separate class, with no consideration of the types of processes that drive acquisition of immunity and its role in disease progression. In an insightful approach, Filipe et al[44] introduced three agespecific "immunityfunctions" in their SEI model for the human host, in which the infected humans are divided into three classes  infected with severe disease (I_{ h1 } ), asymptomatic patent infection (I_{ h2 } ), and infected with undetectable parasite density (I_{ h3 } ). The effect of mosquito density is incorporated through the force of infection (h). The dynamics of transmission of infection in this model is given in Additional file 1 (Table S4).
The three immunity functions (IF) introduced in the Filipe model are  (i) Reducing the susceptibility to clinical disease, ϕ (IF1), (ii) speeding up of the clearance of detectable parasites, r_{ A } (IF2), and (iii) increasing tolerance to subpatent infections, r_{ ij } (IF3). These functions depend on age and disease transmission intensity (i.e., Entomological Inoculation Rate, see Table 1) in a complex manner. They base their model assumptions on the fact that the rates at which both types of immunity  clinical and antiparasite  develop are different. Details of the immunity functions are given in Additional file 2. All these processes have widely varied time scales, which make the disease transmission in this agestructured population quite complex. The first two types of immune functions reproduced the epidemiological ageprevalence curves seen in empirical data better. The third one i.e. the tolerance to subpatent infections, is not required to explain the empirical data.
Hostpathogen variability and resistant strain models
The basic models assume homogeneity in the host and parasite populations in terms of their response to the process of transmission of infection. They consider all the individual hosts and parasites in the population to have an equal chance of developing disease or becoming immune or transmitting infection. However modern application of molecular typing methods has shown that there exist diversity among host and parasites in responding to infection. Further, long term and indiscriminate use of insecticides (DDT) and drugs (quinine and chloroquine) brought forth the hitherto neglected issue of heterogeneity in vector and parasite phenotypes and genotypes. The evolutionary consequences of these interventions had serious negative impact in malaria control. Most models of population heterogeneity and resistance consider withinhost processes. Many mathematical models have been developed with pathogen population structure and heterogeneous host population to describe variable antigenic response, immune selection, pathogen strain structure [76–78]. Inclusion of evolution of drug resistance, along with other factors, in the models can help in the design of rational strategies for the control of drug resistance [79–86].
Several resistantstrain models have been developed based on evolution of drug resistance through host immunity [82, 85], and by considering the practical implications of the artemisinin combination therapy (ACT) drug policies adopted by many countries [84]. Population genetic considerations of the cost of resistance (Table 1), are also included in this type of models [81, 87]. More recent work elaborates the complexity of the process of drug resistance by considering the interaction of several environmental, pharmacological and genetic factors [86]. These models are important as they address phenomena critical to public health, i.e., the evolution of drug resistance in malaria parasites.
In general, these resistantStrain models divide the infected host population (I_{ h } ) into two compartments, i.e., infected by drugsensitive strain and drugresistant strain of the parasite.
where, ∈ is termed as the "Effectiveness of treatment", i.e., the ratio of the duration of infection for the untreated and treated sensitive parasites, and Γ is the cost of resistance. Thus, these two parameters (Γ and ∈) regulate whether drug sensitive or resistant parasite will be dominant in the population. The model also shows that, in the absence of drug or treatment, the fitness of resistant parasite reduces with respect to sensitive parasite; otherwise both the parasites have identical properties. In this case, sensitive and resistant parasites cannot coexist.
Environmental factors
The epidemiology of the host, vector, and pathogen for malaria necessitates consideration of the conditions that increase the mosquito population density. Modelling the dynamics of mosquito populations to increase understanding of malaria transmission across a range of environmental conditions, including climate change, is an important and emerging research area. The basic reproductive numbers (R_{ 0 } ) for the basic models depend crucially on the parameters related to mosquito density. Environmental factors, such as temperature, humidity, rainfall and wind patterns have great impact on mosquito reproduction, development and longevity and the parasite survival in its life cycle in mosquito. It is known that mosquito breeding is influenced by temperature  a change in temperature from 12°C to 31°C reduces the number of days required for breeding from 65 days to 7.3 days [88]. The sporogony of the parasites in vector is completed in 55 days at 16°C, which reduces to 7 days at 28°C [89]. With recent surge of interest on the effects of global warming on malaria incidence, modelling the effects of environmental factors in malaria transmission has become quite relevant and topical [6–8, 90, 91]. As humidity is conducive to mosquito growth, rainfall and stagnant water bodies also influence mosquito density.
The environmental impact on the transmission of malaria is therefore studied primarily by modifying the mosquito population dynamics. Influence of temperature and humidity change on the rate of transformation from juveniles to adults in the susceptible class of adult mosquitoes has been modelled [88]. In addition, several mathematical studies have been performed to simulate the effect of environmental variability in the abundance of mosquito populations such as, random fluctuation in the form of colour noise in infected mosquito dynamics of Ross model [92], periodic or noisy form of the force of infection [12, 41, 92]. Several studies have also included the effect of environmental fluctuations in diverse ways [2, 5, 67, 68, 93, 94] with the aim to develop realistic and validated malaria modelling frameworks that are able to identify the crucial linkages between pathogen transmission processes and climactic factors.
In a recent study, Parham and Michael proposed a model [5], to study the dynamics of the mosquito population by considering simultaneous effects of rainfall and temperature. The model consists of three compartments in humans (S_{ h }, I_{ h }, R_{ h } ) with fixed duration of latency, and three compartments in mosquitoes (S_{ m }, E_{ m }, I_{ m } ) (see Additional file 1 Table S6). Different environmental factors are introduced in this model through parameters related to mosquitoes. The birth rate of adult mosquito is considered to be a function of rainfall and temperature, whereas, mosquito mortality rate, biting rate, duration of sporogonic cycle and survival probability of infected mosquitoes over the incubation period of the parasite are considered to be dependent on temperature variation. The major finding of this model is that changes in rainfall patterns not only influence vector abundance, but also strongly govern malaria endemicity, invasion and extinction. However, when sufficient rainfall exists to sustain vector development and survival, then the temperature affects the pathogen life cycle, and has stronger influence on the rate of disease spread.
Social and economic factors
Malaria risk is highly dependent on the socioeconomic conditions of the host population. It is now fairly evident that "as a general rule of thumb, where malaria prospers most, human societies have prospered least" [95]. Poverty is largely concentrated in the tropical and subtropical zones, and that is where most malaria transmission is observed. The extent of the correlation suggests that malaria and poverty are intimately related. In most endemic areas of malaria, changes in social and economic conditions are considered to be far more important than temperature shift [68]. The economic and social burdens from factors such as fertility, population growth, premature mortality, misdiagnosis, inflicted by the disease on the society have been studied by many authors [95–100]. Given the nature of the factors, most investigations are case studies, and there are only few differential equation based models that incorporate socioeconomic structure.
Using a mathematical model (see Additional file 1 Table S3), Yang showed how the basic reproductive number (R_{ 0 } ) of malaria transmission changes with global warming and local social and economic conditions [68]. In this model good, intermediate and poor economic conditions among human community have been considered and each condition is further divided into three temperature zones. A host of factors control disease transmission rates in this model such as, differential immunity, endemicity, resistance, economic conditions and temperature dependence of mosquito development. These lead to different R_{ 0 } for three temperature zones with different socioeconomic structures. These modelling results point out the requirement of proper management of the surrounding environment, along with good health care system, in disease transmission. From the point of view of designing field research, it is shown in a mosquito based model [101], that the effectiveness of malaria control through different types of intervention methods (insecticidetreated nets and indoor residual spraying) can have differential protection, with the former being more protective. The socioeconomic scenario for large scale deployment of interventions at the population level has also been addressed using modelling studies [102].
Migration and visitation
One of the important reasons for the failure of strategies to eradicate infectious disease is because of their neglect of the mobility patterns of the host. The importance of the role of human migration is evident in the recent increase in malaria incidence not only in the endemic zones, but also in zones where malaria had been eradicated [17, 103–105]. Mainly two types of mobility patterns that can spread the infection to newer areas, are considered  migration, i.e., when the people move from one region to another with no returns; and visitation, when the people return to their original region after visiting other regions.
The effects of migration and visitation on transmission of malaria were shown by TorresSorando and Rodriguez by modifying the basic Ross model to include space that is fragmented into A number of patches [48]. Only humans are assumed to move among the patches and mosquitoes are evenly distributed. The equations describing the system are shown in Additional file 1 (Table S7i). The number of infected humans and mosquitoes in patch i (i = 1, 2,....., A) at time t are, I_{ hi } (t) and I_{ mi } (t), respectively, and e_{ ij } is the fraction of humans that migrate per unit time from patch i to patch j, and do not return. So, to incorporate the effect of migration in this model, an extra term related to infected people due to movement between the patches, i.e. , is added to the human dynamics equation. For visitation, the individuals visit from patch i to patch j during a time, which is a fraction T_{ ij } of the time unit, and then return to their patch of origin. The number of newly infected people added during visitation is, . The corresponding equations for the visitation model are shown in Additional file 1 (Table S7ii). The model results show that increase in mobility between patches enhances the persistence of the disease. Even though migration of humans does not change the equilibrium prevalence, equilibrium is reached faster for higher levels of migration. When there is visitation, the equilibrium prevalence increases with visitation time, and the time to reach the equilibrium decreases with increase in the intensity of visitation.
The elaboration of different approaches to model malaria transmission in populations, as described in Figure 2, is given in the earlier sections. As mentioned earlier, only few models have been discussed above as examples to demonstrate the manner in which different factors and variables relevant to the hostvectorparasite biology have been incorporated in the basic models. Because of the overwhelming complexity of the disease system and its nonlinear interdependence on the environmental and socioeconomic factors, there has not been one consensus general model where all factors are included. Linking the withinhost and betweenhost dynamics of malaria has been one approach towards a comprehensive model [26, 27]. Some effort has also been directed to develop software that include many factors for simulating the disease output [23, 106].
Stochastic models
Plasmodium lifecycle and mosquito population density are highly dependent on different internal processes and external environmental factors, which are probabilistic in nature. In many of the models referred above, stochasticity has been included in different ways. Even when the main structure of the compartments is similar to the differential equation based models, stochasticity has been included through individual variability in individual based models [24, 27], and probabilistic variation in different variables and parameters of transmission processes and environmental factors [27, 107–110]. Models integrating stochasticity with other factors such as, spatial contact structure and temporal forcing, also explain many interesting features of disease transmission [111, 112]. Though not discussed in detail here, these models are powerful tools to explain complex interactions characterized by realistic descriptions.
Data based statistical modelling
One of the major uses of a model is to fit past data and predict the future trend. This capability of a model improves the credibility of the underlying hypothesis of the model. In general epidemiological data represent the disease prevalence in an area over a period of time (time series data), and is given by the number of cases and number of infected persons. As has been enumerated in the earlier sections, this number, representing the prevalence of the disease, is the result of a large number of interacting nonlinear processes in hostpathogen interaction  both deterministic and probabilistic. These processes may have variable contribution to the development of the infection depending on the type of disease  a few may have larger contribution in the process, and others can be peripheral. Statistical modelling involves developing relationships between these factors in the form of mathematical equations. Statistical models that fit curves of past temporal prevalence of a disease, do not make any assumptions about the internal mechanisms that a mathematical model provides. This modelling approach involves application of a variety of elaborate statistical methodologies and tests, and has been extensively used in describing and predicting (forecasting) malaria incidence in different regions.
Several results from the models described in the previous sections have been validated with experimental data. Incorporating age and immunity, Aron [73] verified the results with the Garki project data [62]. To show the effect of socio economic conditions and environmental factors, Yang tested his model results with malaria data from three regions  (i) disease free but potentially under risk (Southeast Brazil: upper bound of temperature 20°C), (ii) disease at low endemic levels (Amazon region and Southeast Asia), and (iii) disease at higher endemic levels (Africa: lower bound of temperature 31°C) [68]. Filipe et al used the data from Northern Tanzania and north and south bank of river Gambia, to verify their model findings [44]. This kind of statistical modelling remains a highly preferred approach in quantitative malaria research to understand the disease incidence and the role of different factors [113].
One useful statistical approach to understand how disease prevalence changes based on other variables such as, temperature or rainfall, is the method of Regression modelling [114]. It involves techniques for modelling and analysis of the relationship between a dependent variable and one or more independent variables. Different variants of this method has been used to model malaria transmission in Africa through the Malaria Risk in Africa (MARA) project and in Europe [115], understanding the effect of childhood population data, climate averages and Normalized Difference Vegetation Index (NDVI) in Mali from 1960 [116], role of temperature and rainfall from meteorological stations and communitybased parasitological survey in the prediction of malaria risk [117], effect of vector abundance, population immunity, on malaria incidence [118], predict the seasonal pattern of malaria in Kenya using NDVI [119] and to show evidence and causality between health and poverty in malaria prevalence [120]. Researchers have also used elaborate time series analysis models to show seasonality patterns in the malaria incidence [121–124].
With the availability of worldwide datasets on population distribution, global circulation, environmental factors, and parasitological prevalence, epidemiologists have now increasingly been interested in global modelling perspectives [89]. Such activities require, along with mathematical models, in depth statistical modelling techniques such as, bayesian inference using Markov chain Monte Carlo method, and multivariate statistical modelling techniques to generate maximum likelihood predictions for posterior probability of parasite distributions on the world map [6, 125–127]. The most reassuring result from these global studies is that contrary to prevailing forecasts of global malaria expansion due to climate change, other natural and anthropogenic forces acting on the disease have actually resulted in a net reduction in transmission.
The above theoretical exercises depend crucially on the quality and availability of data and the methodologies used to build the models. They yield limited predictability and understanding due to factors such as, lack of information or excessive complexity. These models also evolve based on newer data and increased understanding of processes involved. Thus the data based analysis in the statistical modelling approach continues to be a major area in malaria research.
Summary and outlook
A model is a mathematical abstraction of reality. The level of abstraction depends on the questions asked and the scale at which the underlying causative processes are studied. For example, in interhost transmission of an infection, most molecular events in hostpathogen interactions (e.g., types of immune cells involved, parasite development inside the host, signaling pathways) are not considered. Many of these processes are condensed into a single parameter in the immune function or inoculation rate. In intrahost models, on the other hand, how the titers of the infective agent or related molecules change in an individual is studied, because that is what decides the diseased state of the host individual. In epidemiological models, intrahost processes between hostparasitevector are neglected, but the host and vector population are subdivided in terms of the infection/diseased state (i.e., Susceptible, Exposed, Infected, and Immune/Recovered). These models aim to match their results with the available epidemiological data, where incidence of the infectious disease (and death due to it) determines the status of an epidemic in the population. Here, from the public health point of view, one is more interested in knowing if the infection will die out, or persist in a population through the important parameter R_{ 0 } . Yet, as more molecular studies are coming to the fore and both detection of infection and mode of infection propagation (genes, proteins, pathways, immune interactions) are elucidated, the epidemiological models also would need to consider these processes for inclusion as parameters or subclasses. This is clearly visible in the later models, where both the infected and recovered classes are divided into subclasses (such as, asymptomatic), which have different time scales and/or transmission modes. In this review, efforts have been taken to group the epidemiological models of malaria in terms of the complexity of infection processes included in its description, which makes them more realistic. The agespecific distribution of infection due to differential immunity across age is one such case. The assumption is that more realistic models would enhance the understanding of the infection transmission process at the population level, which, in turn, may help in better prediction of intervention strategies. The specific models discussed here are only indicative and not exhaustive. Pure mathematical analysis of the models, even though not so popular among the biologists, is important. They allow clear understanding of the logic of the system behaviour in terms of the relationship among the parameters and variables, which are representative to real biological processes. It will be useful to develop connections between mathematical analysis and their real world implications, since such analyses may help us to understand hitherto unknown scenario, such as the effect of temperature, seasonal forcing, excessive rainfall, correlation between different variables and parameter changes. Among the innumerable statistical models based on malaria incidence data, only a few approaches have been described here. The results of these models are mostly data specific and applicable primarily to the particular data set studied. They are highly useful for prediction in that specific context, but may not work in other places/scenarios. Models that incorporate the essentials of hostparasitevector interaction, proper clinical population subdivisions for disease transmission and also describe multiple data sets from different ecological regions, promise to be an ideal combination of both approaches. It is now clear that the role of indirect factors such as, social structure, economic status, play an overwhelming role in the transmission and persistence of malaria in a region. It also underlies the failure of several control measures where local heterogeneity was not considered. It is the need of the hour to include factors such as the role of heterogeneity in host population due to social status, local differences in ecology due to poverty, differential effects of disease transmission in populations residing in habitats of different temperature, in the mathematical models. Such a description has the possibility of yielding understanding of malaria transmission for populations with societal differences and climate change. Mathematical models have the ability to address several multiplicative, feedback and nonlinear effects that enhance or suppress the effects of factors such as, exposure, immunity, spatiotemporal heterogeneities, control measures and environment, in order to capture key linkages to the complex transmission dynamics. They can also include stochasticity in different variables and parameters to simulate realistic scenario. This comparative analysis of different mathematical models of malaria would contribute to consolidate our understanding about the evolution of these models, and may also help in developing new models by incorporating features discussed above to improve predictions and deciding realistic control measures.
Declarations
Acknowledgements
The authors are grateful to the anonymous referees for valuable comments and suggestions in improving the content and C. Suguna for critcally reading the manuscript. Department of Science and Technology, India is acknowledged for financial support (SR/SO/AS25/2008).
Authors’ Affiliations
References
 World Health Organization (WHO) and WHO Global Malaria Programme.http://www.who.int/malaria/about_us/en/index.html, [http://www.who.int/topics/malaria/en/]
 Martens WJM, Niessen LW, Rotmans J, Jetten TH, McMichael AJ: Potential Impact of global climate change on malaria risk. Environ Health Perspect. 1995, 103: 458464. 10.1289/ehp.95103458.PubMed CentralPubMedGoogle Scholar
 Hay SI, Cox J, Rogers DJ, Randolph SE, Stern DI, Shanks GD, Myers MF, Snow RW: Climate change and the resurgence of malaria in East African highlands. Nature. 2002, 415: 905909. 10.1038/415905a.PubMed CentralPubMedGoogle Scholar
 Tanser FC, Sharp B, le Sueur D: Potential effect of climate change of malaria transmission in Africa. Lancet. 2003, 362: 17921798. 10.1016/S01406736(03)148982.PubMedGoogle Scholar
 Parham PE, Michael E: Modeling the effects of weather and climate change on malaria transmission. Environ Health Perspect. 2010, 118: 620626.PubMed CentralPubMedGoogle Scholar
 Gething PW, Smith DL, Patil AP, Tatem AJ, Snow RW, Hay SI: Climate change and the global malaria recession. Nature. 2010, 465: 342346. 10.1038/nature09098.PubMed CentralPubMedGoogle Scholar
 Evengård B, Sauerborn R: Climate change influences infectious diseases both in the Arctic and the tropics: joining the dots. Global Health Action. 2009, 2:Google Scholar
 Peterson AT: Shifting suitability for malaria vectors across Africa with warming climates. BMC Infect Dis. 2009, 9: 5910.1186/14712334959.PubMed CentralPubMedGoogle Scholar
 Pattanayak S, Sharma VP, Kalra NL, Orlov VS, Sharma RS: Malaria paradigms in India and control strategies. Indian J Malar. 1994, 31: 141199.Google Scholar
 Holt RA, Subramanian GM, Halpern A: The genome sequence of the malaria mosquito Anopheles gambiae. Science. 2002, 298: 129149. 10.1126/science.1076181.PubMedGoogle Scholar
 Gardner MJ, Hall N, Fung E: Genome sequence of the human malaria parasite Plasmodium falciparum. Nature. 2002, 419: 498511. 10.1038/nature01097.PubMedGoogle Scholar
 Anderson RM, May RM: Infectious diseases of humans: dynamics and control. 1991, London: Oxford University PressGoogle Scholar
 The malERA Consultative Group on Modeling: A Research agenda for malaria eradication: modeling. PLoS Med. 2011, 8: e1000403PubMed CentralGoogle Scholar
 Roll Back Malaria Partnership: The global malaria action plan, for a malaria free world. 2008, Geneva, Switzerland, [http://www.rbm.who.int/gmap/index.html]
 Alonso PL, Brown G, Arevalo M, Binka F, Chitnis C, Collins F, Doumbo O, Greenwood B, Hall L, Levine M, Mendis K, Newmann R, Plowe C, Rodriguez MH, Sinden R, Slusker L, Tanner M: A research agenda to underpin malaria eradication. PLoS Med. 2011, 8: e100040610.1371/journal.pmed.1000406.PubMed CentralPubMedGoogle Scholar
 Kermack WO, McKendrick AG: Contribution to the mathematical theory to epidemics. Proc R Soc Lond Series A. 1922, 115: 100121.Google Scholar
 Bailey NTJ: The Biomathematics of malaria. 1982, London: Charles Griffin and Co LtdGoogle Scholar
 Hoshen MB, Heinrich R, Stein WD, Ginsburg H: Mathematical modelling of the withinhost dynamics of Plasmodium falciparum. Parasitology. 2001, 121: 227235.Google Scholar
 Dietz K, Raddatz G, Molineaux L: Mathematical model of the first wave of Plasmodium falciparum asexual parasitemia in nonimmune and vaccinated individuals. Am J Trop Med Hyg. 2006, 75: 4655.PubMedGoogle Scholar
 Mideo N, Day T, Read AF: Modelling malaria pathogenesis. Cell Microbiol. 2008, 10: 19471955. 10.1111/j.14625822.2008.01208.x.PubMed CentralPubMedGoogle Scholar
 McQueen PG, McKenzie FE: Host control of malaria infections: constraints on immune and erythropoeitic response kinetics. PLoS Comput Biol. 2008, 4: 115. 10.1371/journal.pcbi.0040001.Google Scholar
 Mackinnon MJ, Marsh K: The Selection Landscape of Malaria Parasites. Science. 2010, 328: 866871. 10.1126/science.1185410.PubMedGoogle Scholar
 Antao T, Hastings IM: ogaraK: a population genetics simulator for malaria. Bioinformatics. 2011, 27: 13351336. 10.1093/bioinformatics/btr139.PubMedGoogle Scholar
 Gu W, Killeen GF, Mbogo CM, Regens JL, Githure JI, Beier JC: An individualbased model of Plasmodium falciparum malaria transmission on the coast of Kenya. Trans R Soc Trop Med Hyg. 2003, 97: 4350. 10.1016/S00359203(03)900186.PubMedGoogle Scholar
 Gu W, Novak RJ: Habitatbased modeling of impacts of mosquito larval interventions on entomological inoculation rates, incidence and prevalence of malaria. Am J Trop Med Hyg. 2005, 73: 546552.PubMedGoogle Scholar
 McKenzie FE, Bossert WH: An integrated model of Plasmodium falciparum dynamics. J Theor Biol. 2005, 232: 411426.PubMedGoogle Scholar
 Smith T, Maire N, Ross A, Penny M, Chitnis N, Schapira A, Studer A, Genton B, Lengeler C, Tediosi F, De Savigny D, Tanner M: Towards a comprehensive simulation model of malaria epidemiology and control. Parasitology. 2008, 135: 15071516. 10.1017/S0031182008000371.PubMedGoogle Scholar
 Killeen GF, McKenzie FE, Foy BD, Schieffelin C, Billingsley PF, Beier JC: A simplified model for predicting malaria entomological inoculation rates based on entomologic and parasitologic parameters relevant to control. Am J Trop Med Hyg. 2000, 62: 535544.PubMed CentralPubMedGoogle Scholar
 McKenzie FE: Why model malaria?. Parasitol Today. 2000, 16: 458464. 10.1016/S01694758(00)018044.Google Scholar
 Gu W, Mbogo CM, Githure JI, Regens JL, Killeen GF, Swalm CM, Yan G, Beiser JC: Low recovery rates stabilize malaria endemicity in areas of low transmission in coastal Kenya. Acta Trop. 2003, 86: 7181. 10.1016/S0001706X(03)000202.PubMedGoogle Scholar
 Smith DL, Dushoff J, McKenzie FE: The risk of mosquitoborne infection in a heterogeneous environment. PloS Biol. 2004, 2: 19571964.Google Scholar
 Chitnis N, Cushing JM, Hyman JM: Bifurcation analysis of a mathematical model for malaria transmission. SIAM J Appl Math. 2006, 67: 2445. 10.1137/050638941.Google Scholar
 Ngwa GA: On the population dynamics of the malaria vector. Bull Math Biol. 2006, 68: 21612189. 10.1007/s115380069104x.PubMedGoogle Scholar
 Parham PE, Ferguson NM: Space and contact networks: capturing the locality of disease transmission. J R Soc Interface. 2006, 3: 483493. 10.1098/rsif.2005.0105.PubMed CentralPubMedGoogle Scholar
 Ross R: The prevention of malaria. 1911, London: John MurrayGoogle Scholar
 Ross R: Some a priori pathometric equations. Br Med J. 1915, 1: 546447. 10.1136/bmj.1.2830.546.PubMed CentralPubMedGoogle Scholar
 Ross R: An application of the theory of probabilities to the study of a priori pathometry  I. Proc R Soc. 1916, A92: 204230.Google Scholar
 Ross R: An application of the theory of probabilities to the study of a priori pathometry  II. Proc R Soc. 1916, A93: 212225.Google Scholar
 Ross R, Hudson HP: An application of the theory of probabilities to the study of a priori pathometry  III. Proc R Soc. 1916, A93: 225240.Google Scholar
 Macdonald G: The epidemiology and control of malaria. 1957, London: Oxford University PressGoogle Scholar
 Aron JL, May RM: The population dynamics of malaria. Population Dynamics of Infectious Disease. Edited by: Anderson RM. 1982, London: Chapman and Hall, 139179.Google Scholar
 Dietz K: Mathematical models for transmission and control of malaria. Principles and Practice of Malariology. Edited by: Wernsdorfer W, McGregor Y. 1988, Edinburgh: Churchill Livingston, 10911133.Google Scholar
 Aron JL: Mathematical modeling of immunity to malaria. Math Biosci. 1988, 90: 385396. 10.1016/00255564(88)900764.Google Scholar
 Filipe JAN, Riley EM, Darkeley CJ, Sutherland CJ, Ghani AC: Determination of the processes driving the acquisition of immunity to malaria using a mathematical transmission model. PLoS Comp Biol. 2007, 3: 25692579.Google Scholar
 Hasibeder G, Dey C: Population dynamics of mosquitoborne disease: persistence in a completely heterogeneous environment. Theor Popul Biol. 1988, 33: 3153. 10.1016/00405809(88)900032.PubMedGoogle Scholar
 Gupta S, Swinton J, Anderson RM: Theretical studies of the effects of heterogeneity in the parasite population on the transmission dynamics of malaria. Proc R Soc Lond B. 1994, 256: 231238. 10.1098/rspb.1994.0075.Google Scholar
 Gupta S, Hill AVS: Dynamic interactions in malaria: host heterogeneity meets parasite polymorphism. Proc R Soc Lond B. 1995, 261: 271277. 10.1098/rspb.1995.0147.Google Scholar
 TorresSorando L, Rodriguez DJ: Models of spatiotemporal dynamics in malaria. Ecol Model. 1997, 104: 231240. 10.1016/S03043800(97)00135X.Google Scholar
 Rodriguez DJ, TorresSorando L: Models of infectious diseases in spatially heterogeneous environments. Bull Math Biol. 2001, 63: 547571. 10.1006/bulm.2001.0231.PubMedGoogle Scholar
 Hethcote HW: A thousand and one epidemic models. Frontiers in mathematical biology. Lecture notes in Biomathematics. Edited by: Simon A Levin. 1984, Springer, 100: 504515.Google Scholar
 Tang YW, Procop GW, Persing DH: Molecular diagnostics of infectious diseases. Clin Chem. 1997, 43: 20212038.PubMedGoogle Scholar
 Johnston SP, Pieniazek NJ, Xayavong MV, Slemenda SB, Wilkins PP, da Silva J: PCR as a confirmatory technique for laboratory diagnosis of malaria. J Clin Microbiol. 2006, 44: 10871089. 10.1128/JCM.44.3.10871089.2006.PubMed CentralPubMedGoogle Scholar
 Nothdurft HD, Jelinek T, Bluml A, Von Sonnenburg F, Loscher T: Seroconversion to circumsporozoite antigen of Plasmodium falciparum demonstrates a high risk of malaria transmission in travelers to East Africa. Clin Infect Dis. 1999, 28: 641642. 10.1086/515155.PubMedGoogle Scholar
 Macdonald G: Epidemiological basis of malaria control. Bull World Health Organ. 1956, 15: 613626.PubMed CentralPubMedGoogle Scholar
 Pampana E: A Textbook of Malaria Eradication. 1969, London: Oxford University PressGoogle Scholar
 Koella JC: On the use of mathematical models of malaria transmission. Acta Trop. 1991, 49: 125. 10.1016/0001706X(91)90026G.PubMedGoogle Scholar
 Dietz K, Molineaux L, Thomas A: A malaria model tested in the African savannah. Bull World Health Organ. 1974, 50: 347357.PubMed CentralPubMedGoogle Scholar
 Fisher RA: The genetical theory of natural selection. 1930, Oxford: Clarendon PressGoogle Scholar
 Macdonald G: The analysis of equilibrium in malaria. Trop Dis Bull. 1952, 49: 813829.PubMedGoogle Scholar
 Dietz K: Transmission and control of arbovirus diseases. Epidemiology. Edited by: Ludwig D, Cooke KL. 1975, Philadelphia:SIAM, 104121.Google Scholar
 Heffernan JM, Smith RJ, Wahl LM: Perspectives on the basic reproductive ratio. J Royal Soc Interface. 2005Google Scholar
 Molineaux L, Gramiccia G: The Garki project: research on the epidemiology and control of malaria in the Sudan savanna of West Africa. 1980, Geneva: World Health OrganizationGoogle Scholar
 Boyd MF: Epidemiology of malaria: factors related to the intermediate host. Malariology. Edited by: Boyd MF. 1949, Philadelphia: WB Saunders Company, 551607.Google Scholar
 Ngwa GA, Shu WS: A mathematical model for endemic malaria with variable human and mosquito populations. Math Comput Model. 2000, 32: 747763. 10.1016/S08957177(00)001692.Google Scholar
 Ngwa GA: Modelling the dynamics of endemic malaria in growing populations. Discrete Contin Dyn Syst  Ser B. 2004, 4: 11731202.Google Scholar
 Chitnis N: Using mathematical models in controlling the spread of malaria. PhD thesis. 2005, University of Arizona, Program in Applied MathematicsGoogle Scholar
 Yang HM: Malaria transmission model for different levels of acquired immunity and temperaturedependent parameters (vector). Revista de Saúde Pública. 2000, 34: 223231.PubMedGoogle Scholar
 Yang HM, Ferreira MU: Assessing the effects of global warming and local social and economic conditions on the malaria transmission. Revista de Saúde Pública. 2000, 34: 214222.PubMedGoogle Scholar
 Okell LC, Drakeley CJ, Bousema T, Whitty CJ, Ghani AC: Modelling the impact of artemisinin combination therapy and longacting treatments on malaria transmission intensity. PLoS Med. 2008, 5: e22610.1371/journal.pmed.0050226.PubMed CentralPubMedGoogle Scholar
 Ghani AC, Sutherland CJ, Riley EM, Drakeley CJ, Griffin JT, Gosling RD, Filipe JAN: Loss of population levels of immunity to malaria as a result of exposurereducing interventions: Consequences for interpretation of disease trends. PLoS ONE. 2009, 4: e438310.1371/journal.pone.0004383.PubMed CentralPubMedGoogle Scholar
 Griffin JT, Hollingsworth TD, Okell LC, Churcher TS, White M, Hinsley W, Bousema T, Drakeley CJ, Ferguson NM, Basáñez MG, Ghani AC: Reducing Plasmodium falciparum malaria transmission in Africa: a modelbased evaluation of intervention strategies. PLoS Med. 2010, 7: e100032410.1371/journal.pmed.1000324.PubMed CentralPubMedGoogle Scholar
 Cairns M, Ghani A, Okell L, Gosling R, Carneiro I, Anto F, Asoala V, OwusuAgyei S, Greenwood B, Chandramohan D, Milligan P: Modelling the Protective Efficacy of alternative delivery schedules for intermittent preventive treatment of malaria in infants and children. PLoS ONE. 2011, 6: e1894710.1371/journal.pone.0018947.PubMed CentralPubMedGoogle Scholar
 Aron JL: Dynamics of acquired immunity boosted by exposure to infection. Math Biosci. 1983, 64: 249259. 10.1016/00255564(83)90007X.Google Scholar
 Shililu , Ghebremeskel T, Mengistu S, Fekadu H, Zerom M, Mbogo C, Githure J, Novak R, Brantly E, Beier JC: High seasonal variation in entomologic inoculation rates in Eritrea, a semiarid region of unstable malaria in Africa. Am J Trop Med Hyg. 2003, 69: 607613.PubMedGoogle Scholar
 KellyHope L, McKenzie FE: The multiplicity of malaria transmission: a review of entomological inoculation rate measurements and methods across subSaharan Africa. Malar J. 2009, 8: 1910.1186/14752875819.PubMed CentralPubMedGoogle Scholar
 Gupta S, Galvani A: The effects of host heterogeneity on pathogen population structure. Phil Trans R Soc Lond B. 1999, 354: 711719. 10.1098/rstb.1999.0424.Google Scholar
 Gupta S, Anderson RM: Population structure of pathogens: the role of immune selection. Parasitol Today. 1999, 15: 497501. 10.1016/S01694758(99)015598.PubMedGoogle Scholar
 Recker M, Nee S, Bull PC, Kinyanjui S, Marsh K, Newbold C, Gupta S: Transient crossreactive immune responses can orchestrate antigenic variation in malaria. Nature. 2004, 429: 555558. 10.1038/nature02486.PubMedGoogle Scholar
 Hastings IM: A model for the origins and spread of drugresistant malaria. Parasitology. 1997, 115: 133141. 10.1017/S0031182097001261.PubMedGoogle Scholar
 Dye C, Williams BG: Multigenic drug resistance among inbred malaria parasites. Proc R Soc London Ser B Biol Sci. 1997, 264: 6167. 10.1098/rspb.1997.0009.Google Scholar
 Koella JC: Costs and benefits of resistance against antimalarial drugs. Parasitol Today. 1998, 14: 360364. 10.1016/S01694758(98)012976.PubMedGoogle Scholar
 Koella JC, Antia R: Epidemiological models for the spread of antimalarial resistance. Malar J. 2003, 2: 310.1186/1475287523.PubMed CentralPubMedGoogle Scholar
 Mackinnon MJ: Drug resistance models for malaria. Acta Tropica. 2005, 94: 207217.PubMedGoogle Scholar
 Pongtavornpinyo W, Yeung S, Hastings I, Dondorp A, Day N, White N: Spread of antimalarial drug resistance: Mathematical model with practical implications for ACT drug policies. Malar J. 2008, 7: 22910.1186/147528757229.PubMed CentralPubMedGoogle Scholar
 Chiyaka C, Garira W, Dube S: Effects of treatment and drug resistance on the transmission dynamics of malaria in endemic areas. Theor Popul Biol. 2009, 75: 1429. 10.1016/j.tpb.2008.10.002.PubMedGoogle Scholar
 Antao T, Hastings IM: Environmental, pharmacological and genetic influences on the spread of drugresistant malaria. Proc R Soc B. 2011, 278: 17051712. 10.1098/rspb.2010.1907.PubMed CentralPubMedGoogle Scholar
 Boëte C, Koella JC: A theoretical approach to predicting the success of genetic manipulation of malaria mosquitoes in malaria control. Malar J. 2002, 1: 310.1186/1475287513.PubMed CentralPubMedGoogle Scholar
 Li J, Welch RM, Nair US, Sever TL, Irwin DE, CordonRosales C, Padilla N: Dynamic Malaria Models with Environmental Changes. Proceedings of the Thirty Fourth Southeastern Symposium on System Theory. 2002, Huntsville, AL, 396400.Google Scholar
 Martens WJM, Niessen L, Rotmans J, Jetten TH, McMichael J: Climate change and vectorborne disease: a global modelling perspective. Global Environ Change. 1995, 5: 195209. 10.1016/09593780(95)00051O.Google Scholar
 Paaijmans KP, Read AF, Thomas MB: Understanding the link between malaria risk and climate. Proc Nat Acad Sci USA. 2009, 106: 1384413849. 10.1073/pnas.0903423106.PubMed CentralPubMedGoogle Scholar
 Jetten TH, Martens WJM, Takken W: Model simulations to estimate malaria risk under climate change. J Med Entomol. 1996, 33: 361371.PubMedGoogle Scholar
 Chattopadhyay J, Sarkar RR, Chaki S, Bhattacharya S: Effects of environmental fluctuations on the occurrence of malignant malariaa model based study. Ecol Model. 2004, 177: 179192. 10.1016/j.ecolmodel.2004.03.001.Google Scholar
 Hoshen MB, Morse AP: A weatherdriven model of malaria transmission. Malar J. 2004, 3: 3210.1186/14752875332.PubMed CentralPubMedGoogle Scholar
 Yé Y, Hoshen M, Kyobutungi C, Louis VR, Sauerborn R: Local scale prediction of Plasmodium falciparum malaria transmission in an endemic region using temperature and rainfall. Global Health Action. 2009, 2:Google Scholar
 Sachs J, Malaney P: The economic and social burden of malaria. Nature. 2002, 415: 680685. 10.1038/415680a.PubMedGoogle Scholar
 Wyse APP, Bevilacqua L, Rafikov M: Simulating malaria model for different treatment intensities in a variable environment. Ecol model. 2007, 206: 322330. 10.1016/j.ecolmodel.2007.03.038.Google Scholar
 Amexo M, Tolhurst R, Barnish G, Bates I: Malaria misdiagnosis: effects on the poor and vulnerable. Lancet. 2004, 364: 18961898. 10.1016/S01406736(04)174461.PubMedGoogle Scholar
 van Lieshout M, Kovats RS, Livermore MTJ, Martens P: Climate change and malaria: analysis of the SRES climate and socioeconomic scenarios. Glob Environ Change. 2004, 14: 8799. 10.1016/j.gloenvcha.2003.10.009.Google Scholar
 Martens P, Kovats RS, Nijhof S, de Vires P, Livermore MTJ, Bradley DJ, Cox J, McMichael AJ: Climate change and future populations at risk from malaria. Global Environmental Change. 1999, 9: S89S107.Google Scholar
 Laxminaraya R: Act now or latter? Economics of malaria resistance. Am J Trop Med Hyg. 2004, 71: 187195.Google Scholar
 Chitnis N, Schapira A, Smith T, Steketee R: Comparing the effectiveness of malaria vectorcontrol interventions through a mathematical model. Am J Trop Med Hyg. 2010, 83: 230240. 10.4269/ajtmh.2010.090179.PubMed CentralPubMedGoogle Scholar
 Killeen GF, Smith TA: Exploring the contributions of bed nets, cattle, insecticides and excitorepellency to malaria control: a deterministic model of mosquito hostseeking behaviour and mortality. Trans R Soc Trop Med Hyg. 2007, 101: 867880. 10.1016/j.trstmh.2007.04.022.PubMed CentralPubMedGoogle Scholar
 Aragón LE: Expansión de la frontera, expansión de la enfermeded: movilidad geográfica y salud en la Amazonia. Enfoque Integral de la Salud Humana en la Amazonia. Edited by: Yarzabal L, Espinal C, Aragón LE. 1992, Imprenta UCV, Caracas, 429456.Google Scholar
 SandiaMago A: Venezuela: malaria y movilidad humana estacional de las comunidades indigenas del rio riecito del estado Apure. Fermentum. 1994, 3: 102123.Google Scholar
 Martens P, Hall L: Malaria on the move: human population movement and malaria transmission. Emerg Infect Dis. 2000, 6: 103109. 10.3201/eid0602.000202.PubMed CentralPubMedGoogle Scholar
 Martens P: Health and climate change. Modelling the impacts of global warming and ozone depletion. 1998, London: Earthscan PublicationsGoogle Scholar
 Smith TA: Estimation of heterogeneity in malaria transmission by stochastic modelling of apparent deviations from mass action kinetics. Malar J. 2008, 7: 1210.1186/14752875712.PubMed CentralPubMedGoogle Scholar
 Saul A: Transmission dynamics of Plasmodium falciparum. Parasitology Today. 1996, 12: 7479. 10.1016/01694758(96)806594.PubMedGoogle Scholar
 Craig MH, Snow RW, Le Sueur D: Climatebased distribution model of malaria transmission in subSaharan Africa. Parasitol Today. 1999, 15: 105111. 10.1016/S01694758(99)013964.PubMedGoogle Scholar
 Gaudart J, Touré O, Dessay N, Dicko A, Ranque S, Forest L, Demongeot J, Doumbo OK: Modelling malaria incidence with environmental dependency in a locality of Sudanese savannah area, Mali. Malar J. 2009, 8: 6110.1186/14752875861.PubMed CentralPubMedGoogle Scholar
 Dangerfield CE, Ross JV, Keeling MJ: Integrating stochasticity and network structure into an epidemic model. J R Soc Interface. 2009, 6: 761774.PubMed CentralPubMedGoogle Scholar
 Parham PE, Michael E: Outbreak properties of epidemic models: The roles of temporal forcing and stochasticity on pathogen invasion dynamics. J Theor Biol. 2011, 271: 19. 10.1016/j.jtbi.2010.11.015.PubMedGoogle Scholar
 Abeku TA: Forecasting malaria incidence from historical morbidity patterns in epidemic prone areas of Ethiopiasimple seasonal adjustment method performs best. Trop Med Int Health. 2002, 7: 851857. 10.1046/j.13653156.2002.00924.x.PubMedGoogle Scholar
 Kirk RE: Statistics: An Introduction. 2008, Belmont, CA: Thomson Learning, Inc., 5Google Scholar
 Kuhn KG, CampbellLendrum DH, Davies CR: A continental risk map of malaria mosquito (Diptera: Culicidae) vectors in Europe. J Med Entomol. 2002, 39: 621630. 10.1603/0022258539.4.621.PubMedGoogle Scholar
 Kleinschmidt J, Bagayoko M, Clarke GPY, Craig M, Le D: A spatial statistical approach to malaria mapping. SauerInternational Epidemiological Association. 2000, 29: 355361.Google Scholar
 Ye Y, Louis VR, Simboro S, Sauerborn R: Effect of meteorological factors on clinical malaria risk among children: an assessment using villagebased meteorological stations and communitybased parasitological survey. BMC Public Health. 2007, 7: 10110.1186/147124587101.PubMed CentralPubMedGoogle Scholar
 Lindblade KA, Walker ED, Wilson ML: Early warning of malaria epidemics in African highlands using Anopheles (Diptera: Culicidae)Indoor resting density. J Med Entomol. 2000, 37: 664674. 10.1603/0022258537.5.664.PubMedGoogle Scholar
 Hay SI, Snow RW, Rogers DJ: From predicting mosquito habitat to malaria seasons using remotely sensed data  practice, problems and perspectives. Parasitol Today. 1998, 14: 306313. 10.1016/S01694758(98)01285X.PubMedGoogle Scholar
 Khanum S, Singh A: Health, Poverty and Human security: Illustrations from malaria in SEA. Regional Health Forum. 2007, s11: 3344.Google Scholar
 Briet O, Vounatsou P, Gunawardene DM, Galppaththy GNL, Amerasinghe PH: Models for shortterm malaria prediction in Sri Lanka. Malar J. 2008, 7: 7610.1186/14752875776.PubMed CentralPubMedGoogle Scholar
 Abellana R, Ascaso C, Aponte J, Saute F, Nhalungo D, Nhacolo A, Alonso P: Spatioseasonal modeling of the incidence rate of malaria in Mozambique. Malar J. 2008, 7: 22810.1186/147528757228.PubMed CentralPubMedGoogle Scholar
 Cancre N, Tall A, Rogier C, Faye J, Sarr O, Trape JF, Spiegel A, Bois F: Bayesian analysis of an epidemiological model of Plasmodium falciparum malarial infection in Ndiop, Senegal. Am J Epidemiol. 2000, 152: 760770. 10.1093/aje/152.8.760.PubMedGoogle Scholar
 Chatterjee C, Sarkar RR: Multistep polynomial regression method to model and forecast malaria incidence. PLoS ONE. 2009, 4: e472610.1371/journal.pone.0004726.PubMed CentralPubMedGoogle Scholar
 Smith DL, McKenzie FE, Snow RW, Hay SI: Revisiting the basic reproductive number for malaria and its implications for malaria control. PLoS Biol. 2007, 5: e4210.1371/journal.pbio.0050042.PubMed CentralPubMedGoogle Scholar
 Hay SI, Guerra CA, Gething PW, Patil AP, Tatem AJ, Noor AM, Kabaria CW, Manh BH, Elyazar IR, Brooker S, Smith DL, Moyeed RA, Snow RW: World malaria map: Plasmodium falciparum endemicity in 2007. PLoS Med. 2009, 6: e1000048PubMed CentralPubMedGoogle Scholar
 Rogers DJ, Randolph SE: The global spread of malaria in a future, warmer world. Science. 2000, 289: 17631766.PubMedGoogle Scholar
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