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

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.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.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.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

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.

<p>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.</p

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.

<p>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 (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0099867#pone-0099867-g001" target="_blank">Figure 1</a>); only a few (5–8) states show significant correlation (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0099867#pone-0099867-t002" target="_blank">Table 2</a>). The annual epidemiology has been scaled by a factor (500 for which marked * and rest with 1000, as indicated) for easy comparison.</p

Showing Abbreviations used for State name, annual (1961–2010) mean minimum and maximum values of temperature, humidity and rainfall averaged over each state.

<p>Showing Abbreviations used for State name, annual (1961–2010) mean minimum and maximum values of temperature, humidity and rainfall averaged over each state.</p

Correlation between observed (E_O) and estimated (E_P and E_V) epidemiology load with different combination of meteorological variables.

<p>Correlation coefficients of 0.8 are above 9.5% level of significance for the degrees of freedom The cases with correlation above 95% level of significance for the degrees of freedom involved are in bold.</p

Inter annual variability in observed and simulated malaria epidemiology.

<p>Inter annual variability in observed (N_BSP) and simulated (N_M) malaria epidemiology for the twelve districts calculated using daily temperature, surface humidity and 24 hour accumulated rainfall. The meteorological parameters (temperature, humidity and rainfall) for each district have been adopted for the corresponding year from NCEP daily reanalysis data. The number in the bracket represents the correlation coefficient between observed and simulated epidemiology for the respective district.</p

A Model of Malaria Epidemiology Involving Weather, Exposure and Transmission Applied to North East India

<div><h3>Background</h3><p>Quantitative relations between weather variables and malaria vector can enable pro-active control through meteorological monitoring. Such relations are also critical for reliable projections in a changing climate, especially since the vector abundance depends on a combination of weather variables, each in a given range. Further, such models need to be region-specific as vector population and exposure depend on regional characteristics.</p> <h3>Methods</h3><p>We consider days of genesis based on daily temperature, rainfall and humidity in given ranges. We define a single model parameter based on estimates of exposure and transmission to calibrate the model; the model is applied to 12 districts of Arunachal Pradesh, a region endemic to malaria. The epidemiological data is taken as blood samples that test positive. The meteorological data is adopted from NCEP daily Reanalysis on a global grid; population data is used to estimate exposure and transmission coefficients.</p> <h3>Results</h3><p>The observed annual cycles (2006–2010) and the interannual variability (2002–2010) of epidemiology are well simulated for each of the 12 districts by the model. While no single weather variable like temperature can reproduce the observed epidemiology, a combination of temperature, rainfall and humidity provides an accurate description of the annual cycle as well as the inter annual variability over all the 12 districts.</p> <h3>Conclusion</h3><p>Inclusion of the three meteorological variables, along with the expressions for exposure and transmission, can quite accurately represent observed epidemiology over multiple locations and different years. The model is potentially useful for outbreak forecasts at short time scales through high resolution weather monitoring; however, validation with longer and independent epidemiological data is required for more robust estimation of realizable skill. While the model has been examined over a specific region, the basic algorithm is easily applicable to other regions; the model can account for shifting vulnerability due to regional climate change.</p> </div

Meteorological variables with observed minimum and maximum daily values.

<p>Meteorological variables used in the model for genesis <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0049713#pone.0049713-Githeko1" target="_blank">[24]</a> and observed minimum and maximum daily values in a year during (2000–2010) in each of the twelve districts. The basic meteorological data is from global NCEP Reanalysis data averaged for the respective district. The first row of values signifies thresholds adopted for mosquito genesis and survival <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0049713#pone.0049713-Githeko1" target="_blank">[24]</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0049713#pone.0049713-Thomson1" target="_blank">[29]</a>.</p

Inter-annual variability in epidemiology over Arunachal Pradesh.

<p>Inter-annual variability in epidemiology of Blood Sample Positive (N_BSP).For the years 2006 (thick line), 2007 (thin line with square point), 2008 (light line with circle point), 2009 (thin line) and 2010 (light line) for the twelve districts in Arunachal Pradesh.</p

Annual cycle of observed and simulated malaria epidemiology: 2008.

<p>Annual cycle of observed (N_BSP) and simulated (N_M) malaria epidemiology based on calculation of N_M using daily temperature, surface humidity and 24 hour accumulated rainfall for the twelve districts for the year of 2008. The meteorological parameters (temperature, humidity and rainfall) for each district have been adopted for the corresponding year form NCEP reanalysis data. The number in the bracket represents the correlation coefficient between observed and simulated epidemiology for the respective district.</p