| 1. |
- 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. |
- Farooq, Zia, 1986-
(författare)
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Navigating epidemics : by leveraging data science and data-driven modelling
- 2024
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Ours is an era of global change—including climate change, land-use change, urbanization, increased mobility of humans, species and goods, and environmental shifts. Concurrently, we are witnessing a tangible increase in the rate of (re)emerging infectious diseases, mostly driven by global change factors. This complex landscape of infectious diseases necessitates strategies underpinned by computational tools such as data-driven models to enhance our understanding, response, and predictions of potential epidemics.In this thesis, I leveraged data science algorithms and developed data-driven models that extend beyond specific pathogens, providing insights to prepare for future epidemics, with a focus on Europe. I delved into three temporal contexts: 1) retrospective analyses to understand the contribution of global change factors—specifically climate change and human mobility—fuelling the disease outbreaks and expansion (papers I & IV), 2) develop model to improve disease severity estimation during an outbreak for immediate response (paper III), and 3) future disease transmission risk trajectories under various projected scenarios of global change (paper II)—each playing a crucial role in proactive public health planning and response.In paper I, we assessed the predictive ability and the influence of eco-climatic factors on West Nile virus (WNV)—a pathogen with multiple hosts and mosqutio-vectors, and of public health concern in Europe. Utilizing an advanced machine learning classifier XGBoost, trained on a diverse dataset encompassing eco-climatic, sociodemographic predictors to the WNV presence/absence data, the model accurately predicted the WNV risk a season ahead. Furthermore, by employing an explainable AI algorithm, we uncovered both local and European-level drivers of WNV transmission. Higher temperatures in summer and spring, along with drier winters, were pivotal in the escalated frequency of WNV outbreaks in Europe from 2010 to 2019.In paper II, we projected the WNV risk under climate change and socioeconomics scenarios by integrating augmenting the outputs of climate ensemble into machine learning algorithms. We projected transmission risk trends and maps at local, national, regional and European scale. We predicted a three to five fold increase in WNV transmission risk during the next few decades (2040-60) compared 2000-2020 under extreme climate change scenarios. The proportion of diseasereported European land areas could increase from 15% to 23-30%, putting 161 to 244 million people at risk. Western Europe remains at largest relative risk of WNV increase under all scenarios, and Northern Europe under extreme scenarios. With the current rate of spread and in the absence of intervention or vaccines the virus will have sustained suitability even under low carbon emission scenarios in currently endemic European regions.In paper III, we developed a method to quantify an important epidemiological parameter-case fatality ratio (CFR)— commonly used measure to assess the disease severity during novel outbreaks. In our model, we accounted for the time lags between the reporting of a cases and that of the case fatalities and the probability distribution of time lags and derived the CFR and distribution parameters using an optimization algorithm. The method provided more accurate CFR estimations earlier than the widely used estimators under various simulation scenarios. The method also performed well on empirical COVID-19 data from 34 countries. In paper IV, we modelled annual dengue importations in Europe and the United States driven by human mobility and climate. Travel rates were modelled using a radiation model based on population density, geographic distance, and travel volumes. Dengue viraemic travellers were computed considering local mosquito bite risk, travel-associated bite probability, and visit duration. A dynamic vector life-stage model quantified the climatic suitability of transmissionpermissive local areas. Dengue importations linearly increased in Europe and the U.S. from 2015-2019, rising by 588% and 390%, respectively, compared to 1996-2000 estimates, driven by increased travel volumes (373%) and dengue incidence rates (30%) from endemic countries. Transmission seasons lengthened by 53% and 15% in Europe and the U.S., respectively, indicating increasingly permissive climates for local outbreaks. These findings apply to other diseases such as chikungunya, Zika, and yellow fever, sharing common intermediate host vectors, namely Aedes mosquitoes.This thesis highlights Europe's increasing vulnerability to infectious diseases due to global change factors, putting millions at risk. It emphasizes the significance of advanced modelling and innovative data streams in anticipating epidemic risks. Developing digital early warning systems to track disease drivers and taking urgent climate change mitigation and adaptation measures are crucial to anticipate and reduce future epidemic risks. The outcomes of this research can be used to develop technology-driven decision support tools to aid public health authorities and policymakers in making evidence-based decisions during and inter-epidemic periods.
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| 3. |
- 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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| 4. |
- Kriit, Hedi Katre, 1990-
(författare)
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Improved health economic assessments of sustainable transport solutions in urban environments
- 2022
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Introduction: Part of the European Strategy to achieve climate neutrality in the transport sector is to increase the proportion of electric vehicles (EVs) and active commuting. Health co-benefits from reduced air pollution and increased active commuting are assumed to follow; however, all dimensions of expected health effects are not quantified nor valued monetarily. Current state-of-the art health impact assessments (HIAs) of air pollution assume immediate change in health with exposure; however, the time-window of importance for health outcomes is unknown. Moreover, the currently applied risk estimate of sick leaves in relation to air pollution is poorly generalizable due to outdated exposure assessment and subjective data on outcome. The overall aim of this thesis is to assess the health economic effects of sustainable urban transport solutions and improve the epidemiological knowledge base of air pollution effects. Methods: The health effects of increased active commuting and the resulting change in air pollution exposure were valued monetarily from a health care perspective, and a cost-effectiveness analysis of investment in bicycle infrastructure was conducted. A health economic assessment from a societal perspective was also conducted for an increased proportion of EVs in the vehicle fleet, considering a change in both exhaust and non-exhaust particles. The exposure-lag response between air pollution and risk for ischemic heart disease (IHD) and stroke was assessed in a multi-cohort study using distributed lag-nonlinear models (DLNMs). A case cross-over study design was applied to estimate the odds of sick leaves in relation to short-term PM2.5 exposure, and production losses were valued using the human capital method. Results: Investing in bicycle infrastructure to enable increased active commuting was estimated to be cost-effective from a health care perspective. An increased proportion of EVs was estimated to decrease population-weighted PM2.5 concentrations without the use of studded winter tires, but was estimated to increase with the current use of studded winter tires in Stockholm Sweden. For a 0-50% use of studded winter tires the health economic costs ranged between €20 and €122 million (M). An independent effect of PM2.5 on sick leaves was estimated to correspond to €2M per year in productivity loss for the population of Stockholm municipality. Exposure time windows closer in time and local sources of air pollution were suggested to be of greater importance for incident IHD and stroke.Conclusions: This thesis has demonstrated the health economic potential in policies seeking to transform the transport sector towards sustainability. Investment in the transport sector could lead to decreased morbidity and decreased monetary burden in the health care sector. Non-exhaust particles should be considered in order to fully assess the health economic effects of EVs. Moreover, the risk estimate of sick leaves in relation to air pollution exposure could be included in international HIAs.
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| 7. |
- Kien, Tran Mai, et al.
(författare)
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Climate Services For Infectious Disease Control: A Nexus Between Public Health Preparedness and Sustainable Development, Lessons Learned From Long-Term Multi Site Time Series Analysis of Dengue Fever in Vietnam
- 2016
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Ingår i: International conference on public health: Accelerating the achievement of sustainable development goals for the improvement and equitable distribution of population health. ; , s. 83-84
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- Background: Climate Services provide valuable information for making actionable, data-driven decisions to protect public health in a myriad of manners. There is mounting global evidence of the looming threat climate change poses to human health, including the variability and intensity of infectious disease outbreaks in Vietnam and other low-resource and developing areas. In light of the Sustainable Development Goals, lessons learned from time-series analysis may inform public health preparedness strategies for sustainable urban development in terms of dengue epidemiology, surveillance, control, and early warnings.Subjects and Methods: Nearly 40 years of spatial and temporal (times-series) dataset of meteorological records, including rainfall, temperature, and humidity (among others) which can be predictors of dengue were assembled for all provinces of Vietnam and associated with case data reported to General Department of Preventive Medicine, Ministry of Health of Vietnam during the same period. Time series of climate and disease variables was analyzed for trends and changing patterns of those variables over time. The time-series statistical analysis methods sought to identify spatial (when possible) and temporal trends, seasonality, cyclical patterns of disease, and to discover anomalous outbreak events, which departed from expected epidemiological patterns and corresponding meteorological phenomena, such as El Nino Southern Oscillation (ENSO).Results: Analysis yielded largely conserved finding with other locations in South East Asia for larger Outbreak years and events such as ENSO. Seasonality, trend, and cycle in many provinces were persistent throughout the dataset, indicating strong potential for Climate Services to be used in dengue early warnings.Conclusion: Even public health practitioners, having adequate tools for dengue control available must plan and budget vector control and patient treatment efforts well in advance of large scale dengue epidemics to curb such events overall morbidity and mortality. Similarly, urban and sustainable development in Vietnam might benefit from evidence linking climate change, and ill-health events spatially and temporally in future planning. Long term analysis of dengue case data and meteorological records, provided a cases study evidence for emerging opportunities that on how refined climate services could contribute to protection of public health.
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