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- 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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| 2. |
- Andersson, Bea Angelica, 1992-
(författare)
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Finding fitness : empirical and theoretical explorations of inferring fitness effects from population-level SNP data
- 2024
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- The distribution of fitness effects (DFE) describes the likelihood that a new mutation has a specific effect on the fitness of an individual in a given population. The shape of the DFE is a result of several factors such as population size, mating system and selective environment, and can in turn influence the evolutionary potential of a species. The DFE has long been a field of intense research, but particularly since molecular methods enabled us to study of genetic variation in organisms empirically. This research has led to the development of several statistical methods that use population-level frequencies of single nucleotide polymorphisms (SNPs) to infer the DFE. However, these methods rely on assumptions about the data and the organism itself, which could potentially affect the accuracy of the inferences. In this thesis, I describe how two major factors – data quality and inbreeding – can affect the accuracy of DFE inferences. I also show how and when to (and when not to) use DFE inference methods based on SNP frequencies.All genomic datasets contain inaccuracies and some level of uncertainty. The data sets are therefore often treated to remove the gaps or less reliable information, such as genotypes with low coverage. Some data sets need heavy filtering, which could reduce the amount of data available for analysis. We show that the choice of filter method affects the size of the final data set and the accuracy of the estimated DFE.Many DFE estimation software assumes random mating within the study population. Unfortunately, this assumption induces some error when trying to estimate the DFE in inbred or selfing species. Some have assumed that this is a result of high rates of homozygosity in the data, and should only be a problem in populations with very high rates of selfing (>99%). We show that accuracy of the estimated DFE decreases already at relatively low rates of selfing (70%) and that removing homozygosity does not improve the accuracy, implying that another mechanism could be causing the error.
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| 3. |
- Isaksson, Hanna, 1995-
(författare)
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Adaptation during the early evolution of multicellularity : mathematical models reveal the impact of unicellular history, environmental stress, and life cycles
- 2024
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Multicellular organisms, such as plants and animals, have independently evolved several times over the last hundreds of millions of years. The evolution of multicellularity has significantly shaped modern ecosystems, yet its origins remain largely unknown. Due to the ancient history and the small size scale of early multicellular organisms, few intact fossils have been preserved. To uncover the origins of large and complex life, researchers have turned to alternative methods such as phylogenetic modeling, experimental evolution, and theoretical frameworks. While these approaches have provided novel insights in the early steps of multicellular evolution, few studies have considered the role of adaptation in these novel life cycles. This thesis addresses the gap in our knowledge by employing mathematical modeling and computer simulations to study adaptation in novel multicellular life cycles.The first paper investigates the effects of unicellular reproduction modes, such as budding or binary fission, on the spread of growth rate mutations. It demonstrates that unicellular history significantly influences the adaptation rate, with budding cells exhibiting greater sensitivity to the spatial distribution of mutations.In Paper II, the role of multicellular reproduction mode for the adaptation of altruistic and selfish mutations is explored. Specifically, the study examines how adaptation is affected when the filaments are exposed to a size-based selective pressure. It reveals that while the adaptation of altruistic mutations is favored by large offspring, the spread of selfish mutations depends on both offspring size and selection strength.While Papers I and II assume deterministic life cycle structures at the multicellular level, paper III investigates the evolution of life cycle regulation when cells use internal information. The model demonstrates that when cells only have access to a limited amount of information, there is significant variation in the types of life cycles that emerge. This suggests that to evolve regulated life cycles, additional mechanisms beyond internal information may be necessary, such as cell communication.Papers I-III explore multicellular life cycles where all cells are of the same type, yet most multicellular organisms have evolved cell differentiation, with specialized cells performing various tasks. In Paper IV, the evolutionary paths leading to differentiated multicellularity are investigated when a unicellular population is exposed to an abiotic (non-evolving) selective pressure. The model reveals that while a wide range of phenotypic backgrounds and environmental conditions may induce differentiation and multicellularity, continued adaptation to the stress eventually leads to reversion to unicellularity. This reversion occurs because as cells adapt to the stress, the costs associated with differentiation and group formation may no longer be justified. One potential strategy to prevent reversion could involve considering biotic selective pressures that can co-evolve with the population.Lastly, paper V delves into organisms composed by combinations of uni- and multicellular species. Utilizing this framework to examine present multi-species multicellularity reveals that the species composition influences both the ease of partnership establishment and its stability. Additionally, these chimeric groups can reproduce through various strategies, including fragmentation and complete dissociation. Leaving the constellation endows organisms with a memory of prior partnerships, enhancing their adaptability in forming new ones. This extension opens up novel evolutionary pathways for further exploration.In summary, this thesis offers new insights into how the life cycle structures of simple multicellular organisms impact mutation accumulation and the acquisition of new traits. The adaptability of organisms plays a pivotal role in fostering higher complexity and paving the way for further evolution. Enhancing our understanding in this domain will continue to illuminate the origins of complex life and elucidate the evolutionary factors underlying the rich diversity of multicellular organisms we encounter today.
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| 4. |
- Brännström, Åke, 1975-, et al.
(författare)
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A Method for Estimating the Number of Infections From the Reported Number of Deaths
- 2022
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Ingår i: Frontiers In Public Health. - : Frontiers Media S.A.. - 2296-2565. ; 9
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Tidskriftsartikel (refereegranskat)abstract
- At the outset of an epidemic, available case data typically underestimate the total number of infections due to insufficient testing, potentially hampering public responses. Here, we present a method for statistically estimating the true number of cases with confidence intervals from the reported number of deaths and estimates of the infection fatality ratio; assuming that the time from infection to death follows a known distribution. While the method is applicable to any epidemic with a significant mortality rate, we exemplify the method by applying it to COVID-19. Our findings indicate that the number of unreported COVID-19 infections in March 2020 was likely to be at least one order of magnitude higher than the reported cases, with the degree of underestimation among the countries considered being particularly high in the United Kingdom.
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| 5. |
- Sjödin, Henrik, et al.
(författare)
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COVID-19 healthcare demand and mortality in Sweden in response to non-pharmaceutical mitigation and suppression scenarios
- 2020
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Ingår i: International Journal of Epidemiology. - : Oxford University Press. - 0300-5771 .- 1464-3685. ; 49:5, s. 1443-1453
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Tidskriftsartikel (refereegranskat)abstract
- BACKGROUND: While the COVID-19 outbreak in China now appears suppressed, Europe and the USA have become the epicentres, both reporting many more deaths than China. Responding to the pandemic, Sweden has taken a different approach aiming to mitigate, not suppress, community transmission, by using physical distancing without lockdowns. Here we contrast the consequences of different responses to COVID-19 within Sweden, the resulting demand for care, intensive care, the death tolls and the associated direct healthcare related costs.METHODS: We used an age-stratified health-care demand extended SEIR (susceptible, exposed, infectious, recovered) compartmental model for all municipalities in Sweden, and a radiation model for describing inter-municipality mobility. The model was calibrated against data from municipalities in the Stockholm healthcare region.RESULTS: Our scenario with moderate to strong physical distancing describes well the observed health demand and deaths in Sweden up to the end of May 2020. In this scenario, the intensive care unit (ICU) demand reaches the pre-pandemic maximum capacity just above 500 beds. In the counterfactual scenario, the ICU demand is estimated to reach ∼20 times higher than the pre-pandemic ICU capacity. The different scenarios show quite different death tolls up to 1 September, ranging from 5000 to 41 000, excluding deaths potentially caused by ICU shortage. Additionally, our statistical analysis of all causes excess mortality indicates that the number of deaths attributable to COVID-19 could be increased by 40% (95% confidence interval: 0.24, 0.57).CONCLUSION: The results of this study highlight the impact of different combinations of non-pharmaceutical interventions, especially moderate physical distancing in combination with more effective isolation of infectious individuals, on reducing deaths, health demands and lowering healthcare costs. In less effective mitigation scenarios, the demand on ICU beds would rapidly exceed capacity, showing the tight interconnection between the healthcare demand and physical distancing in the society. These findings have relevance for Swedish policy and response to the COVID-19 pandemic and illustrate the importance of maintaining the level of physical distancing for a longer period beyond the study period to suppress or mitigate the impacts from the pandemic.
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