The Role of Temperature for Malaria Transmission in Gongi Kolela District, Amhara Regional State, North West Ethiopia

Brhanie, Tsegahun Worku

doi:10.4172/2161-1165.1000281

Cited by 3 publications

(5 citation statements)

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“…However, an increase in temperatures does not mean an increase in malaria transmission if accompanied by a decrease in rainfall, since lack of water prevents development of the vector. At low temperatures, the vector needs to survive longer for the saprogenic cycle to take place, while temperatures above 30°C counteracts mosquito survival (Mordecai et al, 2013;Brhanie, 2016) (Table 1).…”

Section: Discussionmentioning

confidence: 99%

“…We mapped the malaria hazard using the ArcGIS raster calculator taking into account related previous work, national malaria guidelines, available reports (FMoH, 2008(FMoH, , 2014 and the different environmental factors as these factors would modulate the core hazard (presence of a malaria case together with capable vectors). Special reference was paid to the temperature as it not only governs vector capability, but also the parasite's requirement inside the vector (Mordecai, 2013;Brhanie, 2016).…”

Section: Variables Investigatedmentioning

confidence: 99%

“…In warmer climates, the adult mosquito digests the blood meal faster and feeds more frequently, while the Plasmodium parasite completes the extrinsic incubation within its anopheline vector in a shorter time leading to increased transmission intensity as well as a higher proportion of infective mosquitoes (Aster, 2010). According to Brhanie (2016), both vector and parasite multiply slowly at cool temperatures with 15°C and 18°C being the minimum, depending on species, which precludes transmission in areas with ambient temperatures below these levels. Based on modelling, Mordecai et al (2013) predict optimal transmission at 25°C with a strong decrease at temperatures above 28°C.…”

Section: Introductionmentioning

confidence: 99%

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Mapping malaria risk using geographic information systems and remote sensing: The case of Bahir Dar City, Ethiopia

Minale¹,

Alemu²

2018

Geospat Health

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The main objective of this study was to develop a malaria risk map for Bahir Dar City, Amhara, which is situated south of Lake Tana on the Ethiopian plateau. Rainfall, temperature, altitude, slope and land use/land cover (LULC), as well as proximity measures to lake, river and health facilities, were investigated using remote sensing and geographical information systems. The LULC variable was derived from a 2012 SPOT satellite image by supervised classification, while 30-m spatial resolution measurements of altitude and slope came from the Shuttle Radar Topography Mission. Metrological data were collected from the National Meteorological Agency, Bahir Dar branch. These separate datasets, represented as layers in the computer, were combined using weighted, multi-criteria evaluations. The outcome shows that rainfall, temperature, slope, elevation, distance from the lake and distance from the river influenced the malaria hazard the study area by 35%, 15%, 10%, 7%, 5% and 3%, respectively, resulting in a map showing five areas with different levels of malaria hazard: very high (11.2%); high (14.5%); moderate (63.3%); low (6%); and none (5%). The malaria risk map, based on this hazard map plus additional information on proximity to health facilities and current LULC conditions, shows that Bahir Dar City has areas with very high (15%); high (65%); moderate (8%); and low (5%) levels of malaria risk, with only 2% of the land completely riskfree. Such risk maps are essential for planning, implementing, monitoring and evaluating disease control as well as for contemplating prevention and elimination of epidemiological hazards from endemic areas.

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Section: Discussionmentioning

confidence: 99%

Section: Variables Investigatedmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Mapping malaria risk using geographic information systems and remote sensing: The case of Bahir Dar City, Ethiopia

Minale¹,

Alemu²

2018

Geospat Health

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“…Vegetation may provide an outdoor resting habitant or shelter for mosquitoes from extreme conditions unfavourable for mosquito-population growth. Many studies have reported associations between changes in malaria burden and patterns of environmental factors [7][8][9][10][11][12][13]. However, the associations reported vary between settings.…”

Section: Introductionmentioning

confidence: 99%

“…However, the associations reported vary between settings. For example, a study from South Africa found that an increase in temperature signi cantly raised malaria infections [12], while another in Ethiopia showed a negative correlation [13].…”

Section: Introductionmentioning

confidence: 99%

Associations between environmental covariates and malaria incidence in high transmission settings of Uganda: A distributed non-linear lagged ecological analysis

Okiring

Routledge

Esptein

et al. 2021

Preprint

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Background Environmental factors such as temperature, rainfall, and vegetation cover play a critical role in malaria transmission. However, quantifying the relationships between environmental factors and measures of disease burden relevant for public health can be complex as effects are often non-linear and subject to temporal lags between when changes in environmental factors lead to changes in the incidence of symptomatic malaria. The study aim was to investigate the associations between environmental covariates and malaria incidence in high transmission settings of Uganda.Methods This study leveraged data from seven malaria reference centres (MRCs) located in high transmission settings of Uganda over a 24-month period (January 2019 - December 2020). Estimates of monthly malaria incidence (MI) were derived from MRCs’ catchment areas. Environmental data including monthy average measures of temperature, rainfall, and normalized difference vegetation index (NDVI) were obtained from remote sensing sources. A distributed non-linear lagged model was used to investigate the quantitative relationship between environmental covariates and malaria incidence. Results Overall, the median (range) monthly temperature was 30oC (26-47), rainfall 133.0 mm (3.0-247), NDVI 0.66 (0.24-0.80) and MI was 790 per 1000 person-years (73-3973). A non-linear relationship between environmental covariates and malaria incidence was observed. An average monthly temperature of 35oC was associated with significant increases in malaria incidence compared to the median observed temperature (30oC) at month lag 2 (IRR: 2.00, 95% CI: 1.42-2.83) and the cumulative increases in MI significantly at month lags 1-4, with the highest cumulative IRR of 8.16 (95% CI: 3.41-20.26) at lag month 4. An average monthly rainfall of 200mm was associated with significant increases in malaria incidence compared to the median observed rainfall (133mm) at lag month 0 (IRR: 1.24, 95% CI: 1.01-1.52) and the cumulative IRR increases of malaria at month lags 1-4, with the highest cumulative IRR of 1.99(95% CI: 1.22-2.27) at lag month 4. An average NVDI of 0.72 was associated with significant cumulative increases in IRR of malaria as compared to the median observed NDVI (0.66) at month lag 2-4, with the highest cumulative IRR of 1.57(95% CI: 1.09-2.25) at lag month 4. The rate of increase in cumulative IRR of malaria was highest within lag months 1-2 as compared to lag months 3-4 for all the environmental covariates.Conclusions In high-malaria transmission settings, high values of environmental covariates were associated with cumulative increases in the incidence of malaria, with peak associations occurring after variable lag times. The complex associations identified are valuable for designing strategies for early warning, prevention, and control of seasonal malaria surges and epidemics.

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Associations between environmental covariates and temporal changes in malaria incidence in high transmission settings of Uganda: a distributed lag nonlinear analysis

et al. 2021

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Background Environmental factors such as temperature, rainfall, and vegetation cover play a critical role in malaria transmission. However, quantifying the relationships between environmental factors and measures of disease burden relevant for public health can be complex as effects are often non-linear and subject to temporal lags between when changes in environmental factors lead to changes in malaria incidence. The study investigated the effect of environmental covariates on malaria incidence in high transmission settings of Uganda. Methods This study leveraged data from seven malaria reference centres (MRCs) located in high transmission settings of Uganda over a 24-month period. Estimates of monthly malaria incidence (MI) were derived from MRCs’ catchment areas. Environmental data including monthly temperature, rainfall, and normalized difference vegetation index (NDVI) were obtained from remote sensing sources. A distributed lag nonlinear model was used to investigate the effect of environmental covariates on malaria incidence. Results Overall, the median (range) monthly temperature was 30 °C (26–47), rainfall 133.0 mm (3.0–247), NDVI 0.66 (0.24–0.80) and MI was 790 per 1000 person-years (73–3973). Temperature of 35 °C was significantly associated with malaria incidence compared to the median observed temperature (30 °C) at month lag 2 (IRR: 2.00, 95% CI: 1.42–2.83) and the increased cumulative IRR of malaria at month lags 1–4, with the highest cumulative IRR of 8.16 (95% CI: 3.41–20.26) at lag-month 4. Rainfall of 200 mm significantly increased IRR of malaria compared to the median observed rainfall (133 mm) at lag-month 0 (IRR: 1.24, 95% CI: 1.01–1.52) and the increased cumulative IRR of malaria at month lags 1–4, with the highest cumulative IRR of 1.99(95% CI: 1.22–2.27) at lag-month 4. Average NVDI of 0.72 significantly increased the cumulative IRR of malaria compared to the median observed NDVI (0.66) at month lags 2–4, with the highest cumulative IRR of 1.57(95% CI: 1.09–2.25) at lag-month 4. Conclusions In high-malaria transmission settings, high values of environmental covariates were associated with increased cumulative IRR of malaria, with IRR peaks at variable lag times. The complex associations identified are valuable for designing strategies for early warning, prevention, and control of seasonal malaria surges and epidemics.

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The Role of Temperature for Malaria Transmission in Gongi Kolela District, Amhara Regional State, North West Ethiopia

Cited by 3 publications

References 5 publications

Mapping malaria risk using geographic information systems and remote sensing: The case of Bahir Dar City, Ethiopia

Mapping malaria risk using geographic information systems and remote sensing: The case of Bahir Dar City, Ethiopia

Associations between environmental covariates and malaria incidence in high transmission settings of Uganda: A distributed non-linear lagged ecological analysis

Associations between environmental covariates and temporal changes in malaria incidence in high transmission settings of Uganda: a distributed lag nonlinear analysis

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