Determining the cutoff of rainfall for Plasmodium falciparum malaria outbreaks in India.

BACKGROUND: Malaria outbreaks due to Plasmodium falciparum have been reported from various parts of India. Rainfall is considered as one of the major determinants for malaria outbreaks, however, an estimate of rainfall threshold for malaria is not known. Owing to the vast geographic area, the present study was planned to determine the amount of rainfall required for malaria outbreaks and the lag period between outbreak and rainfall in different Indian climatic regions. METHODS: Simple statistical methods of overall mean and moving mean (case/mean ratio, CMR) were used to identify the districts with P. falciparum malaria outbreak due to rainfall and the amount of rainfall required for outbreaks. Of 120 districts reporting P. falciparum malaria outbreaks, 99 districts having substantially low number of cases (<100); <3 CMR in any year from 2009 to 2012 and districts having stable malaria transmission were excluded. Finally, analysis of outbreak month, lag period and the threshold of rainfall were determined in respect of 21 districts which represent different agro-climatic zones in the country. Whenever the moving mean of cases attained the value ≥3 in any month, that month was identified as P. falciparum malaria outbreak. The threshold amount of rainfall critical for malaria outbreak was calculated by the average mean of previous months (i.e., lag period). RESULTS: The rainfall cutoff ranged from >70 to >600mm in different districts. The month of outbreak varied with the climatic zones viz. arid, semi-arid, humid and per-humid districts. In humid and per-humid districts, outbreaks occurred during monsoon period whereas in arid and semi-arid regions outbreaks occurred during the post monsoon period. The lag period varied from 1-3 months; long lag period was observed in arid, semi-arid region, while short lag period in humid and per-humid regions. CONCLUSION: Malaria outbreaks can occur in post monsoon in arid and semi-arid region whereas in summer/monsoon in humid and per-humid regions. The lag period between rainfall and outbreak differs from one month with short lag (humid and per-humid) to long lag (arid and semi-arid). Based on the determined threshold of rainfall, the findings would be helpful in forewarning of imminent outbreak of malaria for the timely preparedness of malaria outbreaks not only in 21 studied districts, but also in several other districts of different climatic regions of India.

Malaria risk map for India based on climate, ecology and geographical modelling.

Mapping the malaria risk at various geographical levels is often undertaken considering climate suitability, infection rate and/or malaria vector distribution, while the ecological factors related to topography and vegetation cover are generally neglected. The present study abides a holistic approach to risk mapping by including topographic, climatic and vegetation components into the framework of malaria risk modelling. This work attempts to delineate the areas of Plasmodium falciparum and Plasmodium vivax malaria transmission risk in India using seven geo-ecological indicators: temperature, relative humidity, rainfall, forest cover, soil, slope, altitude and the normalized difference vegetation index using multi-criteria decision analysis based on geographical information system (GIS). The weight of the risk indicators was assigned by an analytical hierarchical process with the climate suitability (temperature and humidity) data generated using fuzzy logic. Model validation was done through both primary and secondary datasets. The spatio-ecological model was based on GIS to classify the country into five zones characterized by various levels of malaria transmission risk (very high; high; moderate; low; and very low. The study found that about 13% of the country is under very high malaria risk, which includes the malaria- endemic districts of the states of Chhattisgarh, Odisha, Jharkhand, Tripura, Assam, Meghalaya and Manipur. The study also showed that the transmission risk suitability for P. vivax is higher than that for P. falciparum in the Himalayan region. The field study corroborates the identified malaria risk zones and highlights that the low to moderate risk zones are outbreak-prone. It is expected that this information will help the National Vector Borne Disease Control Programme in India to undertake improved surveillance and conduct target based interventions.

Mapping of Malaria Vectors at District Level in India: Changing Scenario and Identified Gaps.

Malaria is one of the six major vector-borne diseases in India, the endemicity of which changes with changes in ecological, climatic, and sociodevelopmental conditions. The anopheline vectors are greatly affected by ecological conditions such as deforestation, urbanization, climate and lifestyle. Despite the advent of tools such as Geographic Information System (GIS), the updated information on the distribution of anopheline vectors of malaria is not available. In India, the plan for vector control is organized at subcentral level but information about vectors is unavailable even at the district level. Therefore, a systematic presentation of vector distribution has been made to provide maps in respect of major vector species. A search of the literature for major vector species, that is, Anopheles culicifacies, Anopheles fluviatilis, Anopheles stephensi, Anopheles minimus, and Anopheles dirus sensu lato, since 1927 till 2015 was carried out. Data have been presented as present, absent, and no information about vector species during pre-eradication (1927-1958), posteradication (1959-1999), and current scenario (2000-2015). Vectors' distribution and malaria endemicity were mapped using Arc GIS. Of 630 districts of India, major vectors An. culicifacies, An. fluviatilis, and An. stephensi were present in 420, 241, and 243 districts, respectively. In 183 districts, there is no information on any major malaria vector species although 27 of them from the states of Arunachal Pradesh, Jharkhand, Manipur, and Mizoram are highly endemic for malaria, having incidences of 2-40 cases/1000/year. The identified gaps in vector distribution, particularly in malaria endemic areas, necessitate further surveys so as to generate the missing information.