Fig 1.
Location of study area (A), and temporal patterns of incidence of P. falciparum (red solid) and P. vivax (blue dashed) (B, C). Boxplots are shown in B to illustrate seasonality, and time series are shown in C to illustrate interannual variation.
Fig 2.
Analysis of spatio-temporal patterns of malaria vivax incidence in the city of Ahmedabad.
The panels show the distribution of the cases normalized by population, with the intensity of the color (from low yellows to high reds) corresponding to the ranking of incidence. There is striking consistency from one year to the next in the places exhibiting the highest burden of the disease. Some of this regularity also extends to the two parasites. See S2 Fig, for comparison with the patterns obtained with SPR (Slide Positivity Rate) as an alternative measure of malaria intensity see S1 Fig.
Fig 3.
Map depicting the two groups of wards (administrative units).
Map depicting the two groups of wards (administrative units), with high and low malaria risk respectively, P. vivax (left) and P. falciparum (right). There are significant differences in the annual malaria incidence between the two regions (p<0.001), for both parasites.
Table 1.
Statistical analysis of differences between the two regions for P. vivax, based on the socioeconomic information of the 2001 census.
Table 2.
Results of the best model for monthly cases as a function of environmental covariates for Plasmodium vivax.
Table 3.
Likelihood comparison of the models showing the covariates included in each model.
The best likelihood is for the model that incorporates seasonality, temperature, two regions and neighbors. The last column shows the result of a likelihood ratio test between the null model (model 1) and each of the other models.
Fig 4.
Comparison of observed and predicted cases with the best model.
In (A), the red line corresponds to the average number of cases per 1000 for the 59 wards. The blue dots correspond to predictions given by the median of 5000 simulations, and the gray bars correspond to the 5th and 95th percentiles. In (B-D), simulations of the model predict the seasonal epidemics of 2009 and 2010 starting from the end of the monsoons (August) under modifications of the observed climate covariates. The different panels show the effect of fixing temperature and/or humidity at their mean monthly values, to remove their effect on the interannual variation of these anomalous years. When the interannual effect of both is removed (B), the model clearly over-estimates the cases. Individual effects are less pronounced (C, D) although predictions are also higher than observations. Our best model has a mean absolute error of 68% for predicting the peak of the epidemic in a year with a high number of cases (2013). Fig 4 (E, left), shows the distribution of model forecasts from 5000 runs for October 2003 based on October 2002 data. Although the mean prediction differs from the observation, almost all (~84%) model simulations resulted in large events for 2003. The figure on the right repeats this hindcast analysis for October 2013 (using data from 2012). Here, we find a reduced but still large (∼87%) probability of a large outbreak.