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Risk Assessment of Dengue Transmission in Bangladesh Using a Spatiotemporal Network Model and Climate Data.

ABSTRACT: Vector-borne disease risk assessment is crucial to optimize surveillance, preventative measures (vector control), and resource allocation (medical supplies). High arthropod abundance and host interaction strongly correlate to vector-borne pathogen transmission. Increasing host density and movement increases the possibility of local and long-distance pathogen transmission. Therefore, we developed a risk-assessment framework using climate (average temperature and rainfall) and host demographic (host density and movement) data, particularly suitable for regions with unreported or underreported incidence data. This framework consisted of a spatiotemporal network-based approach coupled with a compartmental disease model and nonhomogeneous Gillespie algorithm. The correlation of climate data with vector abundance and host-vector interactions is expressed as vectorial capacity-a parameter that governs the spreading of infection from an infected host to a susceptible one via vectors. As an example, the framework is applied for dengue in Bangladesh. Vectorial capacity is inferred for each week throughout a year using average monthly temperature and rainfall data. Long-distance pathogen transmission is expressed with human movement data in the spatiotemporal network. We have identified the spatiotemporal suitability of dengue spreading in Bangladesh as well as the significant-incidence window and peak-incidence period. Analysis of yearly dengue data variation suggests the possibility of a significant outbreak with a new serotype introduction. The outcome of the framework comprised spatiotemporal suitability maps and probabilistic risk maps for spatial infection spreading. This framework is capable of vector-borne disease risk assessment without historical incidence data and can be a useful tool for preparedness with accurate human movement data.

SUBMITTER: Riad MH

PROVIDER: S-EPMC8045636 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Bangladesh is one of the most vulnerable countries to climate change impacts also struck by the COVID-19 pandemic. The lockdown measures were ineffective with no sign of flattening the curve. Therefore, the high risk of transmission is evident with an increasing number of affected people. Under this circumstance, a multiple hazards scenario can be developed in this country due to climatic hazards such as cyclones, floods, landslides, heat waves, and the outbreak of infectious diseases such as dengue, cholera, and diarrhoea. The country experiences simultaneously the global pandemic, exceptionally prolonged flood along with the recovery stage from the damages due to the cyclone (Amphan). Therefore, these multiple factors have been putting pressure on losing millions of homes, livelihoods, and agricultural crops. This study aimed to assess the potential impact of a simultaneous strike of climatic hazards and infectious disease outbreaks and their possible strategic management in Bangladesh under different scenarios. A mixed methodological approach was followed in this study including a questionnaire survey, in-depth discussion with experts, and extensive literature review to assess the multi-hazard scenario in a resource-limited setting with high population density. A set of statistical techniques were used to analyze the responses (n = 1590) from different social groups (healthcare professionals, academicians, students, Government and NGO officials, and businessman) under three scenarios. The results revealed the high possibility of aggravating the impact of COVID-19 pandemic if there is a climatic hazard such as flood, cyclone have appeared. The majority of the respondents agreed that the situation will become more devastating if there is another outbreak of diseases such as dengue, cholera, and diarrhoea. The poor and fragile healthcare system of this country cannot bear such unprecedented pressure. The lack of risk assessment and communication, lack of sectoral coordination might restrict the contingency plan of the government. Therefore, considering the unprecedented worst cases a stringent strategic plan for emergency response, short term and long-term management should have to be formulated. Resilience building through proactive planning and implementation of integrated, inclusive and sustainable strategies will be effective to ensure the health and socio-economic security for multi-hazard threats in the country.

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Project description:BackgroundDengue fever, a mosquito-borne viral disease, is a rapidly emerging public health problem in Ecuador and throughout the tropics. However, we have a limited understanding of the disease transmission dynamics in these regions. Previous studies in southern coastal Ecuador have demonstrated the potential to develop a dengue early warning system (EWS) that incorporates climate and non-climate information. The objective of this study was to characterize the spatiotemporal dynamics and climatic and social-ecological risk factors associated with the largest dengue epidemic to date in Machala, Ecuador, to inform the development of a dengue EWS.MethodsThe following data from Machala were included in analyses: neighborhood-level georeferenced dengue cases, national census data, and entomological surveillance data from 2010; and time series of weekly dengue cases (aggregated to the city-level) and meteorological data from 2003 to 2012. We applied LISA and Moran's I to analyze the spatial distribution of the 2010 dengue cases, and developed multivariate logistic regression models through a multi-model selection process to identify census variables and entomological covariates associated with the presence of dengue at the neighborhood level. Using data aggregated at the city-level, we conducted a time-series (wavelet) analysis of weekly climate and dengue incidence (2003-2012) to identify significant time periods (e.g., annual, biannual) when climate co-varied with dengue, and to describe the climate conditions associated with the 2010 outbreak.ResultsWe found significant hotspots of dengue transmission near the center of Machala. The best-fit model to predict the presence of dengue included older age and female gender of the head of the household, greater access to piped water in the home, poor housing condition, and less distance to the central hospital. Wavelet analyses revealed that dengue transmission co-varied with rainfall and minimum temperature at annual and biannual cycles, and we found that anomalously high rainfall and temperatures were associated with the 2010 outbreak.ConclusionsOur findings highlight the importance of geospatial information in dengue surveillance and the potential to develop a climate-driven spatiotemporal prediction model to inform disease prevention and control interventions. This study provides an operational methodological framework that can be applied to understand the drivers of local dengue risk.

Project description:BackgroundClimate variability influences the population dynamics of the Aedes aegypti mosquito that transmits the viruses that cause dengue, chikungunya and Zika. In recent years these diseases have grown considerably. Dengue is now the fastest-growing mosquito-transmitted disease worldwide, putting 40 per cent of the global population at risk. With no effective antiviral treatments or vaccines widely available, controlling mosquito population remains one of the most effective ways to prevent epidemics. This paper analyses the temporal and spatial dynamics of dengue in Mexico during 2000-2020 and that of chikungunya and Zika since they first appeared in the country in 2014 and 2015, respectively. This study aims to evaluate how seasonal climatological variability affects the potential risk of transmission of these mosquito-borne diseases. Mexico is among the world's most endemic countries in terms of dengue. Given its high incidence of other mosquito-borne diseases and its size and wide range of climates, it is a good case study.MethodsWe estimate the recently proposed mosquito-borne viral suitability index P, which measures the transmission potential of mosquito-borne pathogens. This index mathematically models how humidity, temperature and precipitation affect the number of new infections generated by a single infected adult female mosquito in a host population. We estimate this suitability index across all Mexico, at small-area level, on a daily basis during 2000-2020.ResultsWe find that the index P predicted risk transmission is strongly correlated with the areas and seasons with a high incidence of dengue within the country. This correlation is also high enough for chikungunya and Zika in Mexico. We also show the index P is sensitive to seasonal climatological variability, including extreme weather shocks.ConclusionsThe paper shows the dynamics of dengue, chikungunya and Zika in Mexico are strongly associated with seasonal climatological variability and the index P. This potential risk of transmission index, therefore, is a valuable tool for surveillance for mosquito-borne diseases, particularly in settings with varied climates and limited entomological capacity.

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Risk Assessment of Dengue Transmission in Bangladesh Using a Spatiotemporal Network Model and Climate Data.

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