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

Goswami, Prashant; Murty, U. S. N.; Mutheneni, Srinivasa Rao; Kukkuthady, Avinash; Krishnan, Swathi Trithala

doi:10.1371/journal.pone.0049713

Cited by 16 publications

(20 citation statements)

References 31 publications

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“…The strong correlation of RVF epidemics with rainfall has previously been noted [2]; consequently we used rainfall as the temporal variable driving our simulation. Other choices, such as temperature [22] or estimated area of standing water [41] can be incorporated to improve the correlation between model and observation. At least two types of problems are impacted by the choice of temporal drivers of epidemics.…”

Section: Discussionmentioning

confidence: 99%

Coupling Vector-host Dynamics with Weather Geography and Mitigation Measures to Model Rift Valley Fever in Africa

McMahon

Manore

Hyman

et al. 2014

Math. Model. Nat. Phenom.

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We present and characterize a multi-host epidemic model of Rift Valley fever (RVF) virus in East Africa with geographic spread on a network, rule-based mitigation measures, and mosquito infection and population dynamics. Susceptible populations are depleted by disease and vaccination and are replenished with the birth of new animals. We observe that the severity of the epidemics is strongly correlated with the duration of the rainy season and that even severe epidemics are abruptly terminated when the rain stops. Because naturally acquired herd immunity is established, total mortality across 25 years is relatively insensitive to many mitigation approaches. Strong reductions in cattle mortality are expected, however, with sufficient reduction in population densities of either vectors or susceptible (ie. unvaccinated) hosts. A better understanding of RVF epidemiology would result from serology surveys to quantify the importance of herd immunity in epidemic control, and sequencing of virus from representative animals to quantify the realative importance of transportation and local reservoirs in nucleating yearly epidemics. Our results suggest that an effective multi-layered mitigation strategy would include vector control, movement control, and vaccination of young animals yearly, even in the absence of expected rainfall.

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Section: Discussionmentioning

confidence: 99%

Coupling Vector-host Dynamics with Weather Geography and Mitigation Measures to Model Rift Valley Fever in Africa

McMahon

Manore

Hyman

et al. 2014

Math. Model. Nat. Phenom.

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“…The procedure for collecting epidemiological data has been already described in an earlier work [37]; in particular, the data did not involve any personal data or identification of the individuals.…”

Section: Methodsmentioning

confidence: 99%

Relative Roles of Weather Variables and Change in Human Population in Malaria: Comparison over Different States of India

et al. 2014

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BackgroundPro-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.MethodWe 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.ResultsFor 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.ConclusionThe 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.

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“…The bioclimatic differences between the regions set a stage for very different malaria transmission dynamics. In Arunachal Pradesh, malaria transmission is seasonal (peaks mid-year) and is driven by vector abundance, which is dependent on temperature, rainfall and humidity (Goswami et al, 2012). The primary vector is Anopheles minimus, with An.…”

Section: Introductionmentioning

confidence: 99%

Relationships of climate and irrigation factors with malaria parasite incidences in two climatically dissimilar regions in India

Lee

Burkhart

Olson

et al. 2016

Journal of Arid Environments

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We examine how climate and irrigation conditions are associated with malaria infection from 1986-1995 in two climatically dissimilar regions in India. We analyze annually averaged malaria parasite incidence (API) and seasonally averaged climate and irrigation variables in western Rajasthan and Arunachal Pradesh. In arid western Rajasthan, API is significantly positively correlated with summer precipitation and soil moisture, and negatively correlated with summer potential evapotranspiration. Irrigation variables during boreal spring show a significant positive correlation with API in the moisture-limited region. In humid Arunachal Pradesh, API is positively related to summer temperature, but negatively related to summer precipitation and spring irrigation variables, while no statistically significant correlations are observed. During the years of high sea surface temperatures (SSTs) in the tropical western Pacific, increasing precipitation (accompanied by decreasing temperature) in northwestern India appears to promote malaria infection in arid western Rajasthan. However, during the same years, decreasing precipitation and increasing temperature result in increasing malaria incidence in humid Arunachal Pradesh. The statistically significant relationships of climate and irrigation factors with API in western Rajasthan suggest that increasing spring irrigation activity and wetter summer climates related to warmer SST in tropical western Pacific could increase malaria risk in the arid climate region.

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

Cited by 16 publications

References 31 publications

Coupling Vector-host Dynamics with Weather Geography and Mitigation Measures to Model Rift Valley Fever in Africa

Coupling Vector-host Dynamics with Weather Geography and Mitigation Measures to Model Rift Valley Fever in Africa

Relative Roles of Weather Variables and Change in Human Population in Malaria: Comparison over Different States of India

Relationships of climate and irrigation factors with malaria parasite incidences in two climatically dissimilar regions in India

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