Association of Climatic Variability, Vector Population and Malarial Disease in District of Visakhapatnam, India: A Modeling and Prediction Analysis

Srimath-Tirumula-Peddinti, Ravi Chandra Pavan Kumar; Neelapu, Nageswara Rao Reddy; Naresh, S.

doi:10.1371/journal.pone.0128377

Cited by 25 publications

(16 citation statements)

References 66 publications

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“…Based on the results, it can be concluded that relative humidity and temperature showed a strong association with malaria incidence, which is consistent with the study by Srimath-Tirumula [56],…”

Section: Maysupporting

confidence: 91%

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: 91%

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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“…This climatic-vector interaction is common in Culex , Aedes , and Anopheles mosquitoes, for example, vector population dynamics are strongly associated with climatic conditions, winter diapause or refuge, and water availability affecting mosquito development [ 46 – 49 ]. This seasonality in vector abundance leads to seasonality of vector-borne infectious diseases [ 41 , 42 , 45 , 49 – 51 ].…”

Section: Discussionmentioning

confidence: 99%

Population dynamics and community structure of Anopheles mosquitoes along the China-Myanmar border

et al. 2015

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BackgroundUnderstanding the ecology of malaria vectors such as species composition and population dynamics is essential for developing cost-effective strategies to control mosquito vector populations.MethodsAdult mosquitoes (n = 79,567) were collected in five villages along the China-Myanmar border from April 2012 to September 2014 using the CDC light trap without bait method. Mosquito community structure, Anopheles species composition and diversity were analyzed.ResultsTwenty species of Anopheles mosquitoes were identified, with An. minimus s.l. accounting for 85 % of the total collections. Mosquito densities varied from 0.05 females per trap per night (f/t/n) to 3.00 f/t/n, with strong seasonality in all sites and densities peaked from June to August. An. minimus s.l. was predominant (accounting for 54–91 % of total captures) in four villages, An. maculatus s.l. was predominant (71 %) in the high elevation village of Dao Nong, and An. culicifacies accounted for 15 % of total captures in the peri-urban area of Simsa Lawk. All 20 species have been captured in the Mung Seng Yang village, 18 and 15 species in Ja Htu Kawng and Na Bang respectively, and nine species in both Simsa Lawk and Dao Nong. Species richness peaked from April to August. Species diversity, species dominance index, and species evenness fluctuated substantially from time to time with no clear seasonality, and varied greatly amongst villages.ConclusionsMosquitoes were abundant in the China-Myanmar bordering agricultural area with clear seasonality. Species composition and density were strongly affected by natural environments. The targeted intervention strategy should be developed and implemented so as to achieve cost-effectiveness for malaria control and elimination along the border areas.Electronic supplementary materialThe online version of this article (doi:10.1186/s13071-015-1057-1) contains supplementary material, which is available to authorized users.

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“…The distribution and abundance of vectors is determined by the interplay of three factors: suitable climatic conditions, habitat for development, and the availability of hosts for food. These factors are not independent, since changes in climate not only directly affect the conditions for vector development but also indirectly alter vegetation cover and the movement of the hosts (Jones et al , Reisen et al , Mills et al , Moore and Messina , Srimath‐Tirumula‐Peddinti et al ). Of the different aspects of climate, temperature has been shown to influence the growth and proliferation of trypanosomes within the tsetse fly (Walshe et al ).…”

Section: Introductionmentioning

confidence: 99%

Seasonal variation of tsetse fly species abundance and prevalence of trypanosomes in the Maasai Steppe, Tanzania

Nnko

Ngonyoka

Salekwa

et al. 2017

Journal of Vector Ecology

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Tsetse flies, the vectors of trypanosomiasis, represent a threat to public health and economy in sub-Saharan Africa. Despite these concerns, information on temporal and spatial dynamics of tsetse and trypanosomes remain limited and may be a reason that control strategies are less effective. The current study assessed the temporal variation of the relative abundance of tsetse fly species and trypanosome prevalence in relation to climate in the Maasai Steppe of Tanzania in 2014-2015. Tsetse flies were captured using odorbaited Epsilon traps deployed in ten sites selected through random subsampling of the major vegetation types in the area. Fly species were identified morphologically and trypanosome species classified using PCR. The climate dataset was acquired from the African Flood and Drought Monitor repository. Three species of tsetse flies were identified: G. swynnertoni (70.8%), G. m. morsitans (23.4%), and G.pallidipes (5.8%). All species showed monthly changes in abundance with most of the flies collected in July. The relative abundance of G. m. morsitans and G. swynnertoni was negatively correlated with maximum and minimum temperature, respectively. Three trypanosome species were recorded: T. vivax (82.1%), T. brucei (8.93%), and T. congolense (3.57%). The peak of trypanosome infections in the flies was found in October and was three months after the tsetse abundance peak; prevalence was negatively correlated with tsetse abundance. A strong positive relationship was found between trypanosome prevalence and temperature. In conclusion, we find that trypanosome prevalence is dependent on fly availability, and temperature drives both tsetse fly relative abundance and trypanosome prevalence.

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Association of Climatic Variability, Vector Population and Malarial Disease in District of Visakhapatnam, India: A Modeling and Prediction Analysis

Cited by 25 publications

References 66 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

Population dynamics and community structure of Anopheles mosquitoes along the China-Myanmar border

Seasonal variation of tsetse fly species abundance and prevalence of trypanosomes in the Maasai Steppe, Tanzania

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