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

Lee, Eungul; Burkhart, John F.; Olson, Sarah H.; Billings, Anthony A.; Patz, Jonathan A.; Harner, E. James

doi:10.1016/j.jaridenv.2015.08.010

Cited by 10 publications

(6 citation statements)

References 42 publications

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“…During the months of March-April-May, a period of low precipitation, medium to higher temperature with lower relative humidity, there is an increase in the number of incidences of malaria. This also agrees with the findings of Lee et al [55], a study conducted in the humid Arunachal Pradesh, India, that suggests decreasing precipitation and increasing temperature resulted in increasing malaria incidence. With the arrival of the monsoon and during the June-July-August-September, the period of high to very high rainfall, higher temperature, and medium to high relative humidity, the malaria incidents were further on the rise.…”

Section: Maysupporting

confidence: 93%

Comparative Analysis of Machine Learning Classifiers for the Prediction of Malaria Incidence Attributed to Climatic Factors

Mohapatra¹,

Tripathi²,

Pal³

et al. 2020

Preprint

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Background: India has a rising rate of malaria as well as a high mortality rate despite awareness and efforts being focused on the issue. Some regions are profoundly affected than others, such as in Odisha, where the prevalence of malaria is nearly a third of the whole country. This study investigated the influence of climate factors on the incidence of malaria in the Sundargarh district in the state of Odisha, India. Methods: Block-wise observed station rainfall data was sourced from the Special Relief Commissioners' (SRC) web portal. Gridded surface maximum temperature and relative humidity data were accessed from the European Center for Medium-range Weather Forecast (ECMWF) reanalysis data archive. Malaria incident data were collected from the Directorate of Public Health, Government of Odisha. WEKA machine learning tool with two classifier techniques, Multi-Layer Perceptron (MLP) and J48 with 10-fold cross-validation, percentile split (66%), and supplied test options, were used for the Malaria prediction. A comparative analysis was carried out on both techniques to ascertain the superior model amongst the two, concerning the prediction accuracy of malaria in the context of a varying climate. Classifier accuracy, Root Mean Square Error (RMSE), Kappa, and ROC scores were the indicators used for the analysis. Results: The results suggested that J48 had exhibited a better skill to MLP and illustrated less error with a positive kappa. In particular, the 10-fold cross-validation method had better performance over the percentile Spilt (66%) and supplied test options. J48 demonstrated less error (RMSE = 0.6), better kappa = 0.63, and higher accuracy = 0.71), suggesting it as most suitable model. Further, seasonal temperature and humidity variation had shown a better association with malaria incidents in comparison to rainfall.Conclusion: The performance of the machine learning methods for Sundargarh was particularly better during the monsoon and post-monsoon when the events are at the peak. The results were encouraging for the utilization of climate forecast for prediction of malaria incidences. It is thus recommended that the J48 classifier machine learning technique could be adopted for the development of malaria early warning system.

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Section: Maysupporting

confidence: 93%

Comparative Analysis of Machine Learning Classifiers for the Prediction of Malaria Incidence Attributed to Climatic Factors

Mohapatra¹,

Tripathi²,

Pal³

et al. 2020

Preprint

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“…However, feedbacks between irrigated agriculture and climate may compound trade-offs among several ecosystem services. Understanding irrigation-induced climate change is critical for informing stakeholder expectations and water management planning (Niles & Mueller, 2016), estimating growing season length and crop phenology (Zipper et al, 2016), forecasting crop disease and pest risks (Jones et al, 2017), and predicting chronic and infectious human disease risks (Kim et al, 2016;Lee et al, 2016). In this study, we use the Wisconsin Central Sands (WCS) as a representative region to quantify irrigation-induced changes to temperature and humidity in the Midwest United States and similar regions where irrigated agriculture is expanding.…”

Section: Studies Of Irrigated Land-use Change In the Midwest Unitedmentioning

confidence: 99%

“…Moreover, reductions in DTR and VPD can impact human health, specifically impacting vulnerable populations such as children and the elderly. For example, reductions in DTR can alleviate human respiratory and circulatory disease symptoms in children (Xie et al, 2017), reduce mortality in elderly populations (Kim et al, 2016), while increasing infectious and vector-borne disease risks in the general population (Lee et al, 2016). Thus, reductions in regional DTR and VPD could have far-reaching socioecological impacts.…”

Section: Con Clus Ionsmentioning

confidence: 99%

Observation of irrigation‐induced climate change in the Midwest United States

Nocco

Smail

Kucharik

2019

Global Change Biology

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Abbreviations: DTR, diurnal temperature range; e a , vapor pressure; ET, evapotranspiration; RET, reference evapotranspiration; T max , daily maximum temperature; T min , daily minimum temperature; VPD, vapor pressure deficit; WCS, Wisconsin Central Sands. AbstractIrrigated agriculture alters near-surface temperature and humidity, which may mask global climate change at the regional scale. However, observational studies of irrigation-induced climate change are lacking in temperate, humid regions throughout North America and Europe. Despite unknown climate impacts, irrigated agriculture is expanding in the Midwest United States, where unconfined aquifers provide groundwater to support crop production on coarse soils. This is the first study in the Midwest United States to observe and quantify differences in regional climate associated with irrigated agricultural conversion from forests and rainfed agriculture. To this end, we established a 60 km transect consisting of 28 stations across varying land uses and monitored surface air temperature and relative humidity for 31 months in the Wisconsin Central Sands region. We used a novel approach to quantify irrigated land use in both space and time with a database containing monthly groundwater withdrawal estimates by parcel for the state of Wisconsin. Irrigated agriculture decreased maximum temperatures and increased minimum temperatures, thus shrinking the diurnal temperature range (DTR) by an average of 3°C. Irrigated agriculture also decreased the vapor pressure deficit (VPD) by an average of 0.10 kPa. Irrigated agriculture significantly decreased evaporative demand for 25% and 66% of study days compared to rainfed agriculture and forest, respectively. Differences in VPD across the land-use gradient were highest (0.21 kPa) during the peak of the growing season, while differences in DTR were comparable year-round. Interannual variability in temperature had greater impacts on differences in DTR and VPD across the land-use gradient than interannual variability in precipitation. These regional climate changes must be considered together with increased greenhouse gas emissions, changes to groundwater quality, and surface water degradation when evaluating the costs and benefits of groundwater-sourced irrigation expansion in the Midwest United States and similar regions around the world. K E Y W O R D S agriculture, climate, groundwater, irrigation, land use, Midwest United States, temperature, vapor pressure deficit | 3473 NOCCO et al.

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“…Its level of endemicity and burden depends mainly on the environmental and ecological factors of the area affected [ 2 , 3 ]. Irrigated schemes provide a suitable environment for mosquito breeding and malaria transmission which needs special consideration in terms of malaria control and intervention selection [ 4 – 6 ]. Additionally, the risk of malaria endemicity is determined by many factors including the area of residence (urban or rural) and the level of interventions coverage [ 7 ].…”

Section: Introductionmentioning

confidence: 99%

Impact of malaria control interventions on malaria infection and anaemia in areas with irrigated schemes: a cross-sectional population-based study in Sudan

et al. 2021

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Background While the overall burden of malaria is still high, the global technical strategy for malaria advocates for two sets of interventions: vector control-based prevention and diagnosis and prompt effective treatment of malaria cases. This study aimed to assess the performance of malaria interventions on malaria infection and anaemia in irrigated areas in Sudan. Methods Based on the Sudan 2016 national malaria indicator survey, data for two states (Gezira and Sennar), characterized by large-irrigated schemes, were analysed. Four community-level malaria interventions were used as contextual variables: utilization of malaria diagnosis, utilization of Artemisinin-based combination therapy (ACT), utilization of long-lasting insecticidal nets (LLINs) and coverage with indoor residual spraying (IRS). Association between these interventions and two outcomes: malaria infection and anaemia, was assessed separately. Malaria infection was assessed in all age groups while anaemia was assessed in children under 5 years. Multilevel multiple logistic regression analysis were conducted. Results Among 4478 individuals involved in this study distributed over 47 clusters, the overall malaria infection rate was 3.0% and 56.5% of the children under 5 years (total = 322) were anaemic. Except for IRS coverage (69.6%), the average utilization of interventions was relatively low: 52.3% for utilization of diagnosis, 33.0% for utilization of ACTs and 18.6% for LLINs utilization. The multi-level multiple logistic regression model showed that only IRS coverage was associated with malaria infection (Odds ratio 0.83 per 10% coverage, 95%Confidence Interval (95%CI) 0.74–0.94, p = 0.003) indicating that a higher level of IRS coverage was associated with less malaria infection. Anaemia was not associated with any intervention (all p values larger than 0.1). Conclusions Malaria transmission in Gezira and Sennar areas is low. IRS, with insecticide to which vectors are susceptible, is an effective malaria control intervention in irrigated schemes. Community utilization of other interventions was not associated with malaria infection in this study. This may be due to the low utilization of these interventions. However, individual use of LLINs provide personal protection. This study failed to establish an association between anaemia and malaria control interventions in low transmission areas. The higher level of malaria infection in urban areas is a cause for concern.

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

Cited by 10 publications

References 42 publications

Comparative Analysis of Machine Learning Classifiers for the Prediction of Malaria Incidence Attributed to Climatic Factors

Comparative Analysis of Machine Learning Classifiers for the Prediction of Malaria Incidence Attributed to Climatic Factors

Observation of irrigation‐induced climate change in the Midwest United States

Impact of malaria control interventions on malaria infection and anaemia in areas with irrigated schemes: a cross-sectional population-based study in Sudan

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