Skip to main content

Table 1 Overview and main description of the models. The model description includes the main characteristics of variant or total parasite growth, the stochastic components, and the data used for fitting.

From: Mechanistic within-host models of the asexual Plasmodium falciparum infection: a review and analytical assessment

General model structure, and parasite dynamics
Models Molineaux et al.    Gatton & Cheng Eckhoff Childs & Buckee Gurarie et al. McKenzie & Bossert
Adapted models   Johnston et al.       
   Challenger et al.      
Publication year 2001 2013 2017 2004 2012 2015 2012 2005
Discrete (2 days time step) or continuous Discrete Discrete Mixed Discrete Discrete Continuous
Patient specific parameters Yes* No No No No Yes** Yes***
Tracks all parasite variants Yes Yes No Yes Yes Yes No No
Number of variants at the start of infection 1 5 5 5
Main assumption on variant switching dynamics Dependent on variant immune response.
Different switching probability for each variant follows geometric distribution
Fast and slow switching variants. Independent of immune response, described in [51] Switching rate per iRBC, thus larger population have higher probability to introduce new variant. Parasites can switch to 7 available variants, out of a total of 50 variants, in each round Different switching networks are investigated.
Network assumed from [33]
Not explicit
Assumed multiplication rates Different for every variant but constant in time (~ \(\mathcal{N}\left( {\mu _{m} = 16,~\sigma _{m} = 10.4} \right)\)) (For overall parasites) different for every time step, but correlated to previous time step 16 (constant) 16 (constant) Variant-dependent but constant in time (~ \(\mathcal{N}\left( {\mu _{m} = 16,~\sigma _{m} = 8} \right)\)) Range between 15–50, median 23.91
Additional assumptions on growth Dependent on RBC availability Dependent on RBC availability Dependent on RBC availability
Variability included in the model: stochastic parameters Multiplication rate, patient specific parameters for the critical densities for innate and general adaptive immune response Critical densities for innate and general adaptive immune response, and growth rate    Most parameters chosen stochastically (for sensitivity analysis) Merozoite invasion probability and replication rate, innate and adaptive immune response efficiency and activation threshold, antigenically distinct variant clusters None
Variability included in the model: probabilistic equations    Effect of immune responses, antibody production, variant switching Variant switching, effect of variant specific and innate immune response   Falls of immune response due to antigenic switch taken at random None
Data used for the fittinga 35 patients1 90 patients2 Malariatherapy data3 4 122 malariatherapy patients 63 malariatherapy patients5
  1. * Patient specific critical densities for innate and general adaptive immune response calculation
  2. ** Model calibrated to each of the infection chart separately to generate 50 best parameter sets for each calibrated malariatherapy data set
  3. *** Model fitted to each of the infection chart separately, thus all parameters take a patient specific value or are averaged across patients
  4. 1 P. falciparum infections classified as spontaneous cures, out of a total of 334 patients in the malariatherapy data
  5. 2 Patients from the malariatherapy dataset not treated to modify the primary parasitaemia, and with no curative or subcurative treatment after the primary attack
  6. 3 Number of patients is not specified. Includes those with high initial peaks and treated during the primary peak
  7. 4 The model focuses on the effect on outcome of varying the parameters, within biologically reasonable ranges
  8. 5 Inoculated with McLendon. From the patients only the days before any drug or other interventions were used, which included a range between 22 and 159 days of infection
  9. a As described in the initial publications, the models might have been re-fitted when updated, implemented in other research projects, or implemented in transmission models