Remotely-sensed, nocturnal, dew point correlates with malaria transmission in Southern Province, Zambia: a time-series study

Background Plasmodium falciparum transmission has decreased significantly in Zambia in the last decade. The malaria transmission is influenced by environmental variables. Incorporation of environmental variables in models of malaria transmission likely improves model fit and predicts probable trends in malaria disease. This work is based on the hypothesis that remotely-sensed environmental factors, including nocturnal dew point, are associated with malaria transmission and sustain foci of transmission during the low transmission season in the Southern Province of Zambia. Methods Thirty-eight rural health centres in Southern Province, Zambia were divided into three zones based on transmission patterns. Correlations between weekly malaria cases and remotely-sensed nocturnal dew point, nocturnal land surface temperature as well as vegetation indices and rainfall were evaluated in time-series analyses from 2012 week 19 to 2013 week 36. Zonal as well as clinic-based, multivariate, autoregressive, integrated, moving average (ARIMAX) models implementing environmental variables were developed to model transmission in 2011 week 19 to 2012 week 18 and forecast transmission in 2013 week 37 to week 41. Results During the dry, low transmission season significantly higher vegetation indices, nocturnal land surface temperature and nocturnal dew point were associated with the areas of higher transmission. Environmental variables improved ARIMAX models. Dew point and normalized differentiated vegetation index were significant predictors and improved all zonal transmission models. In the high-transmission zone, this was also seen for land surface temperature. Clinic models were improved by adding dew point and land surface temperature as well as normalized differentiated vegetation index. The mean average error of prediction for ARIMAX models ranged from 0.7 to 33.5%. Forecasts of malaria incidence were valid for three out of five rural health centres; however, with poor results at the zonal level. Conclusions In this study, the fit of ARIMAX models improves when environmental variables are included. There is a significant association of remotely-sensed nocturnal dew point with malaria transmission. Interestingly, dew point might be one of the factors sustaining malaria transmission in areas of general aridity during the dry season.

. * Importing casedata and climate variables for the High Zone .
. gen wtime=weekly(wtime1, "yw")  . * Here we see that there is still a unit root, i.e a stochastic trend and we need to differentiate the incidence for it to be stationary. . . . . * Next step is to look at the univariate correlations between incidence and environmental variables as well as which lag times that hold significant patterns.
. . * We use var to analyze this and create lagged variables when associations less than or similar to p=0.1 are seen. We look at lags 1-11 weeks prior to incidence . . * but only use those with a biologically plausible coefficient and lag time.
The test of the variance against zero is one sided, and the two-sided confidence interval is truncated at zero.
The test of the variance against zero is one sided, and the two-sided confidence interval is truncated at zero.
The test of the variance against zero is one sided, and the two-sided confidence interval is truncated at zero.
The test of the variance against zero is one sided, and the two-sided confidence interval is truncated at zero.     The test of the variance against zero is one sided, and the two-sided confidence interval is truncated at zero.
The test of the variance against zero is one sided, and the two-sided confidence interval is truncated at zero.
The test of the variance against zero is one sided, and the two-sided confidence interval is truncated at zero.
The test of the variance against zero is one sided, and the two-sided confidence interval is truncated at zero.
The test of the variance against zero is one sided, and the two-sided confidence interval is truncated at zero.
The test of the variance against zero is one sided, and the two-sided confidence interval is truncated at zero.
The test of the variance against zero is one sided, and the two-sided confidence interval is truncated at zero.  (7) = 46.09 Log likelihood = -7.725932 Prob > chi2 = 0.0000 The test of the variance against zero is one sided, and the two-sided confidence interval is truncated at zero.
The test of the variance against zero is one sided, and the two-sided confidence interval is truncated at zero.
The test of the variance against zero is one sided, and the two-sided confidence interval is truncated at zero.
The test of the variance against zero is one sided, and the two-sided confidence interval is truncated at zero.
The test of the variance against zero is one sided, and the two-sided confidence interval is truncated at zero.
The test of the variance against zero is one sided, and the two-sided confidence interval is truncated at zero.
The test of the variance against zero is one sided, and the two-sided confidence interval is truncated at zero. Prob > chi2 = 0.0000          Prob > chi2 = 0.0000          Prob > chi2 = 0.0000  The test of the variance against zero is one sided, and the two-sided confidence interval is truncated at zero. Prob > chi2 = 0.0000   Prob > chi2 = 0.0000

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The test of the variance against zero is one sided, and the two-sided confidence interval is truncated at zero. Prob > chi2 = 0.0000   The test of the variance against zero is one sided, and the two-sided confidence interval is truncated at zero. The test of the variance against zero is one sided, and the two-sided confidence interval is truncated at zero. Prob > chi2 = 0.0000        Prob > chi2 = 0.0000     Prob > chi2 = 0.0000                     The test of the variance against zero is one sided, and the two-sided confidence interval is truncated at zero.      . . . ** We see here that M37 and M202 are the best models in terms of AIC. Therefore we use these two to create . . ** predictions for the study period, now involving the fitting period -(2012w19 -2013w36) as well as the . . ** evaluation period (2011w19-2012w18). Then we recalculate the logtransformation, creating numbers in actual predicted incidence.               . . . . * In our study, we choose to use 4weekly forecasts. Therefore we first calculate the residuals for each week comparing forecast with the actual incidence just . . * as the MAE was calculated for the evaluation period. See example below. However, this was done in Excel and is not included here more than by example.