Figure 1.
Spatial distribution of malaria prevalence in Bangladesh.
Figure 2.
Time series of the number of all malaria cases per month and meteorological data in Rangamati, 1989–2008.
Figure 3.
Relationship between the number of malaria cases per month and (a) average mean temperature, (b) total rainfall, (c) average relative humidity and (d) Normalized difference vegetation index (NDVI) over lags of 0–3 months (shown as a 3 d.f. natural cubic spline) adjusted for seasonal variation and between-year variations.
RR represents the relative risk of malaria (scaled against the mean monthly number of cases). The centre line in each graph shows the estimated spline curve, and the upper and lower lines represent the 95% confidence limits.
Figure 4.
Relationship between the number of malaria cases per month and (a) average mean temperature, (b) total rainfall, (c) average relative humidity and (d) Normalized difference vegetation index (NDVI) over lags of 0–3 months (shown as a 3 d.f. natural cubic spline) adjusted for potential mutual confounding between these 4 variables, seasonal variation and between-year variations.
RR represents the relative risk of malaria (scaled against the mean weekly number of cases). The centre line in each graph shows the estimated spline curve, and the upper and lower lines represent the 95% confidence limits.
Figure 5.
Relationship between the number of malaria cases per month and (a) average sea surface temperature (SST) of the Bay of Bengal over lags of 0–3 months, (b) average NINO3 over lags of 0–3 months, (c) 4–7 months and (d) 8–11 months (shown as a 3 d.f. natural cubic spline) adjusted for potential mutual confounding between the lags of NINO3, seasonal variation and between-year variations.
RR represents the relative risk of malaria (scaled against the mean monthly number of cases). The centre line in each graph shows the estimated spline curve, and the upper and lower lines represent the 95% confidence limits.