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- Ramadona, Aditya L., 1982-
(författare)
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Spatiotemporal prediction of arbovirus outbreak risk : the role of weather and population mobility
- 2021
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Background: Arboviruses such as dengue and chikungunya have been a significant public health burden globally for several decades. In Indonesia, all four dengue serotypes are circulating. Considering that Indonesian children are exposed to dengue early in life, and secondary infection is more likely to cause severe dengue, the population of Indonesia is confronting a high potential risk of severe dengue. Severe complications such as hemorrhage can develop and lead to fatal outcomes. There exists no specific treatment for dengue infection, but symptomatic treatment can be effective to prevent deaths. Consequently, vector control has become a critical component for controlling dengue transmission, but it is currently often triggered as a reactive response to observed outbreak clusters. Based on disease surveillance, it thus remains challenging to implement vector control efficiently to prevent outbreaks. While meteorological conditions have shown to be predictive of dengue incidence over space and time, it has rarely been used to predict outbreaks at a fine-scale intra-urban level. Further, as the propagation of dengue outbreaks and the introduction of viruses has been found to be associated with human mobility, predictive models combining meteorological conditions with granular mobility data hold promise to provide more predictive models. The objectives in this thesis were to 1) describe the influence of temperature, rainfall, and past dengue cases, and population mobility on dengue risk; 2) develop and validate spatiotemporal models of dengue outbreak risk at fine-scale at the intra-urban level; 3) to utilize new data to assess the emergence and spread of chikungunya in an outbreak situation.Methods: Initially, multivariate time series regression models were established to analyze the risk of dengue corresponding to monthly mean temperature, cumulative rainfall, and past dengue case. Following that, we investigated the potential use of geotagged social media data as a proxy of population mobility to estimate the effect of dengue virus importation pressure in urban villages. Subsequently, we employed distributed lag non-linear models with a Spatiotemporal Bayesian hierarchical model framework to determine the exposure-lag-response association between the risk of dengue and meteorological data while allowing the spatial covariance to be informed by mobility flows. Finally, we validated the selected best-fitted model by its predictive ability using an unseen dataset to mimic an actual situation of an early warning system in use.Results: We found that an optimal combination of meteorology and autoregressive lag terms of past dengue cases was predictive of dengue incidence and the occurrence of dengue epidemics. Subsequently, when we integrated mobility data our results suggested that population mobility was an essential driver of the spread of dengue within cities when combined with information on the local circulation of the dengue virus. The geotagged Twitter data was found to provide important information on presumably local population mobility patterns which were predictive and can improve our understanding of the direction and the risk of spread.Conclusions: A spatiotemporal prediction model was developed that predicted a prognosis of dengueat fine spatial and temporal resolution. Subsequently, such a prognosis can be used as the foundation for developing an early warning system to more effectively deploy vector control prior to the establishment of local outbreak clusters. These findings have implications for targeting dengue control activities at the intraurban villages level, especially in the light of ever increasing population growth, mobility and climate change.
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| 2. |
- Liyanage, Prasad, 1975-
(författare)
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The Influence of Climate and Public Health Interventions on Aedes Vectors and Dengue in Sri Lanka
- 2022
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Introduction: Dengue, a viral infection transmitted by Aedes mosquitos, flourishes in urban tropical environments by a complex process. Interactions among susceptible humans, dengue viruses, and Aedes mosquitoes determine dengue transmission patterns, and these interactions are modified by driving factors related to weather, the environment, and human behaviour, including mobility. Understanding the drivers of dengue and evaluating the effectiveness and costeffectiveness of existing vector control policies are vital to developing evidence-based and timely interventions.Methods: The exposure-lag-response associations between weather variables, Aedes vector indices and dengue at each sub-district Medical Officer of Health (MOH) divisions in Kalutara district, Sri Lanka, were estimated using distributed lag non-linear models. These estimates were meta-analyzed to obtain the average estimates for the district, while exploring the heterogeneities among MOH divisions. Non-linear extension to the interrupted time series analysis was used to evaluate the impact of nation wide mobility restrictions implemented during COVID-19 pandemic on dengue risk at each district, at different age groups in the western province and at the climate zones in Sir Lanka. The effects of the vector control interventions implemented through the civil military cooporation (CIMIC) on dengue were estimated at Panadura MOH division of Kalutara district using interrupted time series analysis while adjusting for potential confounders. The costeffectiveness of the CIMIC intervention was evaluated using a decision analytical modelling framework.Results: We found that El Niño, rainfall, temperature and Aedes larval indices were associated with each other, and dengue, at lag intervals expanding from one to six months. The nation wide mobility restriction was associated with a statistically significant reduction in dengue risk in all climate zones in Sri Lanka. The highest impact was observed among the children age less than 19 years. We found that the CIMIC intervention reduced dengue risk by 50% and was cost-effectivein a defined area.Conclusion: The manifestation of dengue is preceded by the biologically plausible latencies of increasing Aedes larvae and the onset of weather events in Kalutara district. When augmented with location-specific information of vector activities, one to six months lead time from the onset of weather events enables public health authorities to set up short, intermediate, and long-term goals for vector control interventions. The observed significant reduction in dengue risk following the national lockdown in Sri Lanka further highlighted the importance of vector control at public places and schools. The findings of these studies suggest that communities affected by dengue can benefit from investments in vector control if interventions are implemented rigorously and coordinated well across sectors. The methodological framework we developed in this doctoral thesis will contribute to the understanding of the local determinants of dengue and the developmentof early warning systems blended with effective and cost-effective vector control interventions in Sri Lanka and beyond.
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