scholarly journals A Bayesian Spatio-Temporal Analysis of Malaria in the Greater Accra Region of Ghana from 2015 to 2019

2021 ◽  
Vol 18 (11) ◽  
pp. 6080
Author(s):  
Elorm Donkor ◽  
Matthew Kelly ◽  
Cecilia Eliason ◽  
Charles Amotoh ◽  
Darren J. Gray ◽  
...  
Keyword(s):  
Malaria Transmission ◽  
Malaria Control ◽  
Negative Binomial ◽  
Malaria Incidence ◽  
Temporal Analysis ◽  
Climatic Variables ◽  
Negative Binomial Regression Model ◽  
District Health ◽  
South Central ◽  
Greater Accra Region

The Greater Accra Region is the smallest of the 16 administrative regions in Ghana. It is highly populated and characterized by tropical climatic conditions. Although efforts towards malaria control in Ghana have had positive impacts, malaria remains in the top five diseases reported at healthcare facilities within the Greater Accra Region. To further accelerate progress, analysis of regionally generated data is needed to inform control and management measures at this level. This study aimed to examine the climatic drivers of malaria transmission in the Greater Accra Region and identify inter-district variation in malaria burden. Monthly malaria cases for the Greater Accra Region were obtained from the Ghanaian District Health Information and Management System. Malaria cases were decomposed using seasonal-trend decomposition, based on locally weighted regression to analyze seasonality. A negative binomial regression model with a conditional autoregressive prior structure was used to quantify associations between climatic variables and malaria risk and spatial dependence. Posterior parameters were estimated using Bayesian Markov chain Monte Carlo simulation with Gibbs sampling. A total of 1,105,370 malaria cases were recorded in the region from 2015 to 2019. The overall malaria incidence for the region was approximately 47 per 1000 population. Malaria transmission was highly seasonal with an irregular inter-annual pattern. Monthly malaria case incidence was found to decrease by 2.3% (95% credible interval: 0.7–4.2%) for each 1 °C increase in monthly minimum temperature. Only five districts located in the south-central part of the region had a malaria incidence rate lower than the regional average at >95% probability level. The distribution of malaria cases was heterogeneous, seasonal, and significantly associated with climatic variables. Targeted malaria control and prevention in high-risk districts at the appropriate time points could result in a significant reduction in malaria transmission in the Greater Accra Region.

scholarly journals A Bayesian spatio-temporal analysis of malaria in the Greater Accra Region of Ghana from 2015 to 2019.

2021 ◽  
Author(s):  
Elorm Donkor ◽  
Matthew Kelly ◽  
Cecilia Eliason ◽  
Charles Amotoh ◽  
Darren Gray ◽  
...  
Keyword(s):  
Malaria Transmission ◽  
Incidence Rate ◽  
Malaria Control ◽  
Malaria Incidence ◽  
Climatic Variables ◽  
Maximum Temperature ◽  
District Health ◽  
South Central ◽  
Malaria Incidence Rate ◽  
Greater Accra Region

Abstract Efforts towards malaria control in Ghana have had positive impacts. However, these efforts need to be locally tailored to further accelerate progress. The aim of this study was to examine the climatic drivers of malaria transmission in the Greater Accra Region and identify inter-district variation of malaria burden. Monthly malaria cases for the Greater Accra Region were obtained from the Ghanaian District Health Information and Management System. Malaria cases were decomposed using the seasonal-trend decomposition, based on locally weighted regression to analyse the seasonality. A Poisson regression model with a conditional autoregressive prior structure was used to quantify associations between climatic variables and malaria risk, and spatial dependence. Posterior parameters were estimated using Bayesian Markov chain Monte Carlo simulation with Gibbs sampling. A total of 1,105,370 malaria cases was recorded in the region from 2015–2019. The overall malaria incidence rate for the region was approximately 1 per 1,000,000 population. Malaria transmission was highly seasonal with an irregular inter-annual pattern. Malaria incidence was found to increase by 0.1% (95% credible interval [CrI]: 0.02–0.16%) for a 1°C rise in monthly maximum temperature lagged at 6 months and 0.2% (95% CrI: 0.5–0.3%) for 1°C rise in monthly minimum temperature without lag. No spatial dependency was observed after accounting for climatic variables. Only five districts located in the south-central part of the region had a malaria incidence rate that was lower than the regional average at > 95% probability level. The distribution of malaria cases was heterogeneous, seasonal and significantly associated with climatic variables. Targeted malaria control and prevention in high-risk districts at the appropriate time points could result in a significant reduction in malaria transmission in the Greater Accra Region.

scholarly journals A Bayesian spatio-temporal analysis of malaria in the Greater Accra Region of Ghana from 2015 to 2019.

2021 ◽  
Author(s):  
Elorm Donkor ◽  
Matthew Kelly ◽  
Cecilia Eliason ◽  
Charles Amotoh ◽  
Prof. Darren J Gray ◽  
...  
Keyword(s):  
Malaria Transmission ◽  
Incidence Rate ◽  
Malaria Control ◽  
Malaria Incidence ◽  
Climatic Variables ◽  
Maximum Temperature ◽  
District Health ◽  
South Central ◽  
Malaria Incidence Rate ◽  
Greater Accra Region

Abstract Background: Efforts towards malaria control in Ghana have had positive impacts. However, these efforts need to be locally tailored to further accelerate progress. The aim of this study was to examine the climatic drivers of malaria transmission in the Greater Accra Region and identify inter-district variation of malaria burden.Methodology: Monthly malaria cases for the Greater Accra Region were obtained from the Ghanaian District Health Information and Management System from 2015 to 2019. Malaria cases were decomposed using the seasonal-trend decomposition, based on locally weighted regression to analyze the seasonality. A Poisson regression model with a conditional autoregressive prior structure was used to quantify associations between climatic variables and malaria risk, and spatial dependence. Posterior parameters were estimated using Bayesian Markov chain Monte Carlo simulation with Gibbs sampling.Results: A total of 1,105,370 malaria cases was recorded in the region from 2015–2019. The overall malaria incidence rate for the region was approximately 1 per 1,000,000 population. Malaria transmission was highly seasonal with an irregular inter-annual pattern during the study period. Malaria incidence was found to increase by 0.1% (95% credible interval [CrI]: 0.02–0.16%) for a 1°C rise in monthly mean maximum temperature lagged at 6 months and 0.2% (95% CrI: 0.5–0.3%) for 1°C rise in monthly mean minimum temperature without lag. No spatial dependency was observed after accounting for climatic variables. Only five districts located in the south-central part of the region had a malaria incidence rate that was lower than the regional average at > 95% probability level.Conclusion: The distribution of malaria cases was heterogeneous, seasonal and significantly associated with climatic variables. Targeted malaria control and prevention in high-risk districts at the appropriate time points could result in a significant reduction in malaria transmission in the Greater Accra Region.

scholarly journals Heterogeneous exposure and hotspots for malaria vectors at three study sites in Uganda

2018 ◽  
Vol 2 ◽  
pp. 32 ◽  
Author(s):  
Su Yun Kang ◽  
Katherine E. Battle ◽  
Harry S. Gibson ◽  
Laura V. Cooper ◽  
Kilama Maxwell ◽  
...  
Keyword(s):  
Malaria Transmission ◽  
Malaria Vector ◽  
Malaria Control ◽  
Negative Binomial ◽  
Indoor Residual Spraying ◽  
Housing Quality ◽  
Environmental Covariates ◽  
Vector Density ◽  
Vector Abundance ◽  
Study Sites

Background: Heterogeneity in malaria transmission has household, temporal, and spatial components. These factors are relevant for improving the efficiency of malaria control by targeting heterogeneity. To quantify variation, we analyzed mosquito counts from entomological surveillance conducted at three study sites in Uganda that varied in malaria transmission intensity. Mosquito biting or exposure is a risk factor for malaria transmission. Methods: Using a Bayesian zero-inflated negative binomial model, validated via a comprehensive simulation study, we quantified household differences in malaria vector density and examined its spatial distribution. We introduced a novel approach for identifying changes in vector abundance hotspots over time by computing the Getis-Ord statistic on ratios of household biting propensities for different scenarios. We also explored the association of household biting propensities with housing and environmental covariates. Results: In each site, there was evidence for hot and cold spots of vector abundance, and spatial patterns associated with urbanicity, elevation, or other environmental covariates. We found some differences in the hotspots in rainy vs. dry seasons or before vs. after the application of control interventions. Housing quality explained a portion of the variation among households in mosquito counts. Conclusion: This work provided an improved understanding of heterogeneity in malaria vector density at the three study sites in Uganda and offered a valuable opportunity for assessing whether interventions could be spatially targeted to be aimed at abundance hotspots which may increase malaria risk. Indoor residual spraying was shown to be a successful measure of vector control interventions in Tororo, Uganda.  Cement walls, brick floors, closed eaves, screened airbricks, and tiled roofs were features of a house that had shown reduction of household biting propensity. Improvements in house quality should be recommended as a supplementary measure for malaria control reducing risk of infection.

scholarly journals A Dynamical and Zero-Inflated Negative Binomial Regression Modelling of Malaria Incidence in Limpopo Province, South Africa

2019 ◽  
Vol 16 (11) ◽  
pp. 2000 ◽  
Author(s):  
Gbenga J. Abiodun ◽  
Olusola S. Makinde ◽  
Abiodun M. Adeola ◽  
Kevin Y. Njabo ◽  
Peter J. Witbooi ◽  
...  
Keyword(s):  
South Africa ◽  
Time Series ◽  
Regression Model ◽  
Negative Binomial ◽  
Malaria Incidence ◽  
Negative Binomial Regression ◽  
Limpopo Province ◽  
Negative Binomial Regression Model ◽  
Climate Variables ◽  
Binomial Regression

Recent studies have considered the connections between malaria incidence and climate variables using mathematical and statistical models. Some of the statistical models focused on time series approach based on Box–Jenkins methodology or on dynamic model. The latter approach allows for covariates different from its original lagged values, while the Box–Jenkins does not. In real situations, malaria incidence counts may turn up with many zero terms in the time series. Fitting time series model based on the Box–Jenkins approach and ARIMA may be spurious. In this study, a zero-inflated negative binomial regression model was formulated for fitting malaria incidence in Mopani and Vhembe―two of the epidemic district municipalities in Limpopo, South Africa. In particular, a zero-inflated negative binomial regression model was formulated for daily malaria counts as a function of some climate variables, with the aim of identifying the model that best predicts reported malaria cases. Results from this study show that daily rainfall amount and the average temperature at various lags have a significant influence on malaria incidence in the study areas. The significance of zero inflation on the malaria count was examined using the Vuong test and the result shows that zero-inflated negative binomial regression model fits the data better. A dynamical climate-based model was further used to investigate the population dynamics of mosquitoes over the two regions. Findings highlight the significant roles of Anopheles arabiensis on malaria transmission over the regions and suggest that vector control activities should be intense to eradicate malaria in Mopani and Vhembe districts. Although An. arabiensis has been identified as the major vector over these regions, our findings further suggest the presence of additional vectors transmitting malaria in the study regions. The findings from this study offer insight into climate-malaria incidence linkages over Limpopo province of South Africa.

scholarly journals Modelling Malaria Incidence in the Limpopo Province, South Africa: Comparison of Classical and Bayesian Methods of Estimation

2020 ◽  
Vol 17 (14) ◽  
pp. 5016
Author(s):  
Makwelantle Asnath Sehlabana ◽  
Daniel Maposa ◽  
Alexander Boateng
Keyword(s):  
South Africa ◽  
Malaria Control ◽  
Bayesian Method ◽  
Negative Binomial ◽  
Climatic Factors ◽  
Classical Method ◽  
Malaria Incidence ◽  
Limpopo Province ◽  
Malaria Control Programme ◽  
Normalised Difference Vegetation Index

Malaria infects and kills millions of people in Africa, predominantly in hot regions where temperatures during the day and night are typically high. In South Africa, Limpopo Province is the hottest province in the country and therefore prone to malaria incidence. The districts of Vhembe, Mopani and Sekhukhune are the hottest districts in the province. Malaria cases in these districts are common and malaria is among the leading causes of illness and deaths in these districts. Factors contributing to malaria incidence in Limpopo Province have not been deeply investigated, aside from the general knowledge that the province is the hottest in South Africa. Bayesian and classical methods of estimation have been applied and compared on the effect of climatic factors on malaria incidence. Credible and confidence intervals from a negative binomial model estimated via Bayesian estimation and maximum likelihood estimation, respectively, were utilized in the comparison process. Overall assumptions underpinning each method were given. The Bayesian method appeared more robust than the classical method in analysing malaria incidence in Limpopo Province. The classical method identified rainfall and temperature during the night to be significant predictors of malaria incidence in Mopani, Vhembe and Waterberg districts. However, the Bayesian method found rainfall, normalised difference vegetation index, elevation, temperatures during the day and night to be the significant predictors of malaria incidence in Mopani, Sekhukhune and Vhembe districts of Limpopo Province. Both methods affirmed that Vhembe district is more susceptible to malaria incidence, followed by Mopani district. We recommend that the Department of Health and Malaria Control Programme of South Africa allocate more resources for malaria control, prevention and elimination to Vhembe and Mopani districts of Limpopo Province.

scholarly journals Malaria epidemiology and stratification of incidence in the malaria elimination setting in Harari Region, Eastern Ethiopia

2020 ◽  
Vol 9 (1) ◽  
Author(s):  
Endashaw Esayas ◽  
Asefa Tufa ◽  
Fekadu Massebo ◽  
Abdulhamid Ahemed ◽  
Ibssa Ibrahim ◽  
...  
Keyword(s):  
Malaria Transmission ◽  
Incidence Rate ◽  
Malaria Elimination ◽  
Malaria Incidence ◽  
Indoor Residual Spraying ◽  
District Level ◽  
District Health ◽  
Eastern Ethiopia ◽  
Intervention Coverage ◽  
Malaria Epidemiology

Abstract Background Ethiopia has shown notable progress in reducing the burden of malaria over the past two decades. Because of this progress, the country has shifted efforts from control to elimination of malaria. This study was conducted to analyse the malaria epidemiology and stratification of incidence in the malaria elimination setting in eastern Ethiopia. Methods A retrospective study was conducted to analyse the epidemiology of malaria by reviewing the district health office data from 2013 to 2019 in Harari Region. In addition, three years of sub-district level malaria data were used to stratify the malaria transmission intensity. Malaria interventions (Long-lasting insecticidal nets [LLIN] and indoor residual spraying [IRS]) employed were reviewed to analyse the intervention coverage at the Regional level. Descriptive statistics were used to show the malaria transmission in terms of years, season and species of the malaria parasite. Incidence rate per 1000 population and death rate per 1 000 000 population at risk were computed using the total population of each year. Results In the Harari Region, malaria incidence showed a more pronounced declining trend from 2017 to 2019. Plasmodium falciparum, P. vivax and mixed infections accounted for 69.2%, 30.6% and 0.2% of the cases, respectively. There was an increment in malaria intervention coverage and improved malaria diagnosis. In the year 2019 the coverage of LLIN and IRS in the Region were 93.4% and 85.1% respectively. The annual malaria incidence rate dropped from 42.9 cases per 1000 population in 2013 to 6.7 cases per 1000 population in 2019. Malaria-related deaths decreased from 4.7 deaths per 1 000 000 people annually in 2013 to zero, and there have been no deaths reported since 2015. The malaria risk appears to be heterogeneous and varies between districts. A higher number of malaria cases were recorded in Erer and Jenella districts, which constitute 62% of the cases in the Region. According to the sub-district level malaria stratification, there was shrinkage in the malaria transmission map and about 70% of the sub-districts have achieved elimination targets. Conclusions In the Harari Region, malaria morbidity and mortality have been significantly declined. Thus, if this achievement is sustained and scaling-up of the existing malaria prevention and control strategies by focusing on those populations living in the higher malaria transmission districts and sub-districts, planning of malaria elimination from the study area might be feasible.

scholarly journals Effects of rainfall, temperature and topography on malaria incidence in elimination targeted district of Ethiopia

2021 ◽  
Vol 20 (1) ◽  
Author(s):  
Desalegn Dabaro ◽  
Zewdie Birhanu ◽  
Abiyot Negash ◽  
Dawit Hawaria ◽  
Delenasaw Yewhalaw
Keyword(s):  
Regression Model ◽  
Malaria Transmission ◽  
Malaria Elimination ◽  
Meteorological Factors ◽  
Malaria Incidence ◽  
Meteorological Variables ◽  
Maximum Temperature ◽  
Negative Binomial Regression Model ◽  
Regional State ◽  
Cross Correlation Analysis

Abstract Background Climate and environmental factors could be one of the primary factors that drive malaria transmission and it remains to challenge the malaria elimination efforts. Hence, this study was aimed to evaluate the effects of meteorological factors and topography on the incidence of malaria in the Boricha district in Sidama regional state of Ethiopia. Methods Malaria morbidity data recorded from 2010 to 2017 were obtained from all public health facilities of Boricha District in the Sidama regional state of Ethiopia. The monthly malaria cases, rainfall, and temperature (minimum, maximum, and average) were used to fit the ARIMA model to compute the malaria transmission dynamics and also to forecast future incidence. The effects of the meteorological variables and altitude were assessed with a negative binomial regression model using R version 4.0.0. Cross-correlation analysis was employed to compute the delayed effects of meteorological variables on malaria incidence. Results Temperature, rainfall, and elevation were the major determinants of malaria incidence in the study area. A regression model of previous monthly rainfall at lag 0 and Lag 2, monthly mean maximum temperature at lag 2 and Lag 3, and monthly mean minimum temperature at lag 3 were found as the best prediction model for monthly malaria incidence. Malaria cases at 1801–1900 m above sea level were 1.48 times more likely to occur than elevation ≥ 2000 m. Conclusions Meteorological factors and altitude were the major drivers of malaria incidence in the study area. Thus, evidence-based interventions tailored to each determinant are required to achieve the malaria elimination target of the country.

scholarly journals Evaluating the Efficacy, Impact and Feasibility of Community-based House Screening as a Complementary Malaria Control Intervention in Southern Africa: a Study Protocol for a Household Randomized Trial.

2021 ◽  
Author(s):  
Peter Onyango Sangoro ◽  
Ulrike Fillinger ◽  
Kochelani Saili ◽  
Theresia Estomih Nkya ◽  
Rose Marubu ◽  
...  
Keyword(s):  
Vector Control ◽  
Malaria Transmission ◽  
Malaria Control ◽  
Malaria Incidence ◽  
Indoor Residual Spraying ◽  
Clinical Malaria ◽  
Sub Saharan Africa ◽  
Substantial Impact ◽  
Transmission Season ◽  
The Impact

Abstract Background: Concerted effort to control malaria has had a substantial impact on transmission of the disease in the past two decades. In areas where reduced malaria transmission is being sustained through insecticide-based vector control interventions, primarily long-lasting insecticidal nets (LLINs) and indoor residual spraying (IRS), non-insecticidal complementary tools will likely be needed to push towards malaria elimination. Once interruption in local disease transmission is achieved, insecticide-based measures can be scaled down gradually and eventually phased out, saving on costs of sustaining control programmes and mitigating any unintended negative health and environmental impacts posed by insecticides. These non-insecticidal methods could eventually replace insecticidal methods of vector control. House screening, a non-insecticidal method, has a long history in malaria control, but is still not widely adopted in sub-Saharan Africa. This study aims to add to the evidence-base for this intervention in low transmission settings by assessing the efficacy, impact and feasibility of house screening in areas where LLINs are conventionally used for malaria control. Methods: A two-armed, household randomized clinical trial will be conducted in Mozambique, Zambia and Zimbabwe to evaluate whether combined use of house screens and LLINs affords better protection against clinical malaria in children between 6 months and 13 years compared to the sole use of LLINs. Eight hundred households will be enrolled in each study area, where 400 households will be randomly assigned the intervention, house screening and LLINs while the control households will be provided with LLINs only. Clinical malaria incidence will be estimated by actively following up one child from each household for 6 months over the malaria transmission season. Cross-sectional parasite prevalence will be estimated by testing all participating children for malaria parasites at the beginning and end of each transmission season using rapid diagnostic tests.CDC light traps and pyrethrum spray catches (PSC) will be used to sample adult mosquitoes and evaluate the impact of house screening on indoor mosquito density, species distribution and sporozoite rates.Discussion: This study will contribute epidemiological data on the impact of house screening on malaria transmission and assess the feasibility of its implementation on a programmatic scale. Trial registration: This trial was retrospectively registered on 11th August 2020. Registration number PACTR202008524310568.

scholarly journals Heterogeneous exposure and hotspots for malaria vectors at three study sites in Uganda

2018 ◽  
Vol 2 ◽  
pp. 32
Author(s):  
Su Yun Kang ◽  
Katherine E. Battle ◽  
Harry S. Gibson ◽  
Laura V. Cooper ◽  
Kilama Maxwell ◽  
...  
Keyword(s):  
Malaria Transmission ◽  
Malaria Vector ◽  
Malaria Control ◽  
Negative Binomial ◽  
Indoor Residual Spraying ◽  
Malaria Risk ◽  
Housing Quality ◽  
Environmental Covariates ◽  
Vector Density ◽  
Study Sites

Background: Heterogeneity in malaria transmission has household, temporal, and spatial components. These factors are relevant for improving the efficiency of malaria control by targeting heterogeneity. To quantify variation, we analyzed mosquito counts from entomological surveillance conducted at three study sites in Uganda that varied in malaria transmission intensity. Methods: Using a Bayesian zero-inflated negative binomial model, validated via a comprehensive simulation study, we quantified household differences in malaria vector density and examined its spatial distribution. We introduced a novel approach for identifying changes in malaria hotspots over time by computing the Getis-Ord statistic on ratios of household biting propensities for different scenarios. We also explored the association of household biting propensities with housing and environmental covariates. Results: In each site, there was evidence for hot and cold spots, spatial patterns associated with urbanicity, elevation, or other environmental covariates. We found some differences in the hotspots in rainy vs. dry seasons or before vs. after the application of control interventions. Housing quality explained a portion of the variation among households in mosquito counts. Conclusion: This work provided an improved understanding of heterogeneity in malaria vector density at the three study sites in Uganda and offered a valuable opportunity for assessing whether interventions could be spatially targeted to be aimed at hotspots of malaria risk. Indoor residual spraying was shown to be a successful measure of vector control interventions in Tororo, Uganda.  Cement walls, brick floors, closed eaves, screened airbricks, and tiled roofs were features of a house that had shown protective effects towards malaria risk. Improvements in house quality should be recommended as a supplementary measure for malaria control.

scholarly journals Lack of community awareness on malaria and its vectors can impede malaria control: A case study in Great Nicobar Islands

2017 ◽  
Vol 12 ◽  
pp. 1-13
Author(s):  
Vidhya Pachalil Thiruvoth ◽  
◽  
Sunish Ittoop Pulikotil ◽  
Keyword(s):  
Malaria Transmission ◽  
Malaria Control ◽  
Malaria Incidence ◽  
Survey Methods ◽  
Control Programme ◽  
Malaria Control Programme ◽  
Nicobar Island ◽  
Cross Sectional Survey ◽  
Community Knowledge ◽  
Nicobar Islands

Andaman and Nicobar Islands has historically been known for its high malaria transmission in the past. The aftermath of tsunami (2004), increased its risk and vulnerability, due to stagnant water bodies. Anopheles sundaicus is the predominant vector responsible for the perennial transmission of malaria in these islands. The Great Nicobar Island being one of the Tehsil of Nicobar District, is an important tourism centre attracting both national and international visitors in large numbers throughout the year. Community knowledge on malaria and its vectors is a pre-requisite for any successful malaria control programme. In order to determine the community knowledge regarding malaria transmission and control, a cross sectional survey was carried out in three villages of Great Nicobar Island, having high malaria incidence. A total of 170 individuals, viz., 70 Nicobarese (tribal) and 100 non-Nicobarese (non-tribal) were enquired. Both quantitative (KAP) and qualitative (FGD) survey methods were employed to collect the data. Among the Nicobarese, 98.6% were aware of the disease, 94.2% had knowledge of its symptoms. Similar observation was found among the non-Nicobarese community. However, knowledge on the bionomics of malaria vector and transmission was observed to be low in both the groups (17-23%). In the community based awareness campaign, field visits to the vector breeding sites is to be emphasized. Involvement of Self Help Groups and school children would facilitate easy dissemination of knowledge on vectors to the community. This improved awareness could help in reducing vector proliferation and form a basis for effective implementation of malaria control programme.

Sign in / Sign up

Export Citation Format

Share Document