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Abstract Inferences from population genomic data provide valuable insights into the demographic history of a population. Likewise, in genomic epidemiology, pathogen genomic data provide key insights into epidemic dynamics and potential sources of transmission. Yet, predicting what information will be gained from genomic data about variables of interest and how different sampling strategies will impact the quality of downstream inferences remains challenging. As a result, population genomics and related fields such as phylodynamics and phylogeography largely lack theory to guide decisions on how best to sample individuals for genomic sequencing. By adopting a sequential decision making framework based on Markov decision processes, we model how sampling interacts with a population’s demographic history to shape the ancestral or genealogical relationships of sampled individuals. By probabilistically considering these ancestral relationships, we can use Markov decision processes to predict the expected value of sampling in terms of information gained about estimated variables. This in turn allows us to very efficiently explore and identify optimal sampling strategies even when the informational value of sampling depends on past or future sampling events. To illustrate our framework, we develop Markov decision processes for three common demographic and epidemiological inference problems: estimating population growth rates, minimizing the transmission distance between sampled individuals and estimating migration rates between subpopulations. In each case, the Markov decision process allows us to identify optimal sampling strategies that maximize the information gained from genomic data while minimizing the associated costs of sampling. 

Abstract Purpose of ReviewPreparing for pandemics requires a degree of interdisciplinary work that is challenging under the current paradigm. This review summarizes the challenges faced by the field of pandemic science and proposes how to address them. Recent FindingsThe structure of current siloed systems of research organizations hinders effective interdisciplinary pandemic research. Moreover, effective pandemic preparedness requires stakeholders in public policy and health to interact and integrate new findings rapidly, relying on a robust, responsive, and productive research domain. Neither of these requirements are well supported under the current system. SummaryWe propose a new paradigm for pandemic preparedness wherein interdisciplinary research and close collaboration with public policy and health practitioners can improve our ability to prevent, detect, and treat pandemics through tighter integration among domains, rapid and accurate integration, and translation of science to public policy, outreach and education, and improved venues and incentives for sustainable and robust interdisciplinary work. 

This paper describes the development of the disease ecology tradition of health and medical geography including some key themes and innovations. It first grounds disease ecology in the history of ecology from the natural sciences and the human ecology traditions within the social sciences. These ecological studies of disease developed in response to limitations in the biomedical approach to studying health and disease that developed after germ theory. While the biomedical approach, which mostly focused on human biology, led to groundbreaking advances in medicine for many decades, it had its limits. Disease ecology applications have modern roots in the decades before and after World War II through colonial and tropical medicine as well as work conducted in an array of other sites, including Nazi Germany, the Soviet Union, and the United States when there were large efforts to create infectious diseases maps and conduct ecological analyses of diseases. Hundreds of disease ecology studies have been implemented on diverse disease systems since World War II. The field progressively broadened in scope, especially during the 1990s and beyond, with several innovations including the application of political ecology approaches to the study of health and disease. Two other recent innovations are summarized through case studies: disease ecology approaches in health intervention research and applications of theory and methods from landscape genetics. The first case study highlights the ecological and geographic heterogeneity associated with the health impacts of drinking‐water tubewell interventions in rural Bangladesh. The paper also considers ‘landscape genetics’ approaches via a case study about influenza that uses modern genetic and spatial tools along with an ecological approach; it describes how the evolution of the virus is related to human‐environment‐animal interactions. The paper concludes by outlining promising future directions for disease ecology, emphasizing the field's ongoing incorporation of new theories and methods.