Relative roles of weather variables and change in human population in malaria: comparison over different states of India

Abstract

Background: Pro-active and effective control as well as quantitative assessment of impact of climate change on malaria requires identification of the major drivers of the epidemic. Malaria depends on vector abundance which, in turn, depends on a combination of weather variables. However, there remain several gaps in our understanding and assessment of malaria in a changing climate. Most of the studies have considered weekly or even monthly mean values of weather variables, while the malaria vector is sensitive to daily variations. Secondly, rarely all the relevant meteorological variables have been considered together. An important question is the relative roles of weather variables (vector abundance) and change in host (human) population, in the change in disease load.

Method: We consider the 28 states of India, characterized by diverse climatic zones and changing population as well as complex variability in malaria, as a natural test bed. An annual vector load for each of the 28 states is defined based on the number of vector genesis days computed using daily values of temperature, rainfall and humidity from NCEP daily Reanalysis; a prediction of potential malaria load is defined by taking into consideration changes in the human population and compared with the reported number of malaria cases.

Results: For most states, the number of malaria cases is very well correlated with the vector load calculated with the combined conditions of daily values of temperature, rainfall and humidity; no single weather variable has any significant association with the observed disease prevalence.

Conclusion: The association between vector-load and daily values of weather variables is robust and holds for different climatic regions (states of India). Thus use of all the three weather variables provides a reliable means of pro-active and efficient vector sanitation and control as well as assessment of impact of climate change on malaria.

Figure 1. Comparison of % of days that satisfies criteria for vector genesis in terms of individual meteorological parameters like temperature (T), humidity (Q) and rainfall (R) and all the three parameters combined over 28 states of India for the period 2001–2010.

Figure 2. Comparison of observed annual epidemiology load (E_O) with epidemiology load based on vector load (E_V) calculated using days of vector genesis and constant human population with potential epidemiology load (E_P) (growth in human population included) over 28 states of India.

The epidemiology is calculated as the number of blood samples that tested positive. The days of vector genesis here represent days in a year that fulfill combined meteorological conditions of temperature, humidity and rainfall for genesis of mosquitoes. The calculated epidemiology has been scaled by a factor (500 for which marked * and rest with 1000, as indicated) for easy comparison.

Figure 3. Comparison of observed annual epidemiology load (E_O) with epidemiology load based on vector load (E_V) calculated using days of vector genesis and constant human population with potential epidemiology load (E_P) (growth in human population included) over 28 states of India.

The annual epidemiology is calculated as the number of blood samples that test positive. The days of vector genesis represent days in a year that fulfill only meteorological condition of temperature for genesis of mosquitoes. With only temperature as the condition for mosquito genesis, the calculated E_P has very little correspondence to the observed E_P (Figure 1); only a few (5–8) states show significant correlation (Table 2). The annual epidemiology has been scaled by a factor (500 for which marked * and rest with 1000, as indicated) for easy comparison.

Figure 4. Comparison of observed annual epidemiology load (E_O) with epidemiology load based on vector load (E_V) calculated using days of vector genesis and constant human population with potential epidemiology load (E_P) (growth in human population included) over 28 states of India.

The annual epidemiology is calculated as the number of blood samples that positive. The days of vector genesis here represent days in a year that fulfill only meteorological condition of rainfall for genesis of mosquitoes. The annual epidemiology has been scaled by a factor (500 for which marked * and rest with 1000, as indicated) for easy comparison.

Figure 5. Comparison of observed annual epidemiology load (E_O) with epidemiology load based on vector load (E_V) calculated using days of vector genesis and constant human population with potential epidemiology load (E_P) (growth in human population included) over 28 states of India.

The annual epidemiology is calculated as the number of blood samples that positive. The days of vector genesis here represent days in a year that fulfill only meteorological condition of humidity for genesis of mosquitoes. The annual epidemiology has been scaled by a factor (500 for which marked * and rest with 1000, as indicated) for easy comparison.

Figure 6. Linear trends in observed and calculated epidemiology (as % of respective standard deviation) (A) calculated based on genesis constraint of only temperature, only humidity, only rainfall and combined for the period of 1961–2010.

(B) Observed epidemiology (E_O), vector load (E_V) and epidemiology potential (E_P) for the 28 states based on data for the period 2001–2010.