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1. Governance structure affects transboundary disease management under alternative objectives

   https://doi.org/10.1186/s12889-021-11797-3  

   Blackwood, Julie C. ; Malakhov, Mykhaylo M. ; Duan, Junyan ; Pellett, Jordan J. ; Phadke, Ishan S. ; Lenhart, Suzanne ; Sims, Charles ; Shea, Katriona ( October 2021 , BMC Public Health)

   The development of public health policy is inextricably linked with governance structure. In our increasingly globalized world, human migration and infectious diseases often span multiple administrative jurisdictions that might have different systems of government and divergent management objectives. However, few studies have considered how the allocation of regulatory authority among jurisdictions can affect disease management outcomes.

   Here we evaluate the relative merits of decentralized and centralized management by developing and numerically analyzing a two-jurisdictionSIRSmodel that explicitly incorporates migration. In our model, managers choose between vaccination, isolation, medication, border closure, and a travel ban on infected individuals while aiming to minimize either the number of cases or the number of deaths.

   We consider a variety of scenarios and show how optimal strategies differ for decentralized and centralized management levels. We demonstrate that policies formed in the best interest of individual jurisdictions may not achieve global objectives, and identify situations where locally applied interventions can lead to an overall increase in the numbers of cases and deaths.

   Our approach underscores the importance of tailoring disease management plans to existing regulatory structures as part of an evidence-based decision framework. Most importantly, we demonstrate that there needs to be a greater consideration of the degree to which governance structure impacts disease outcomes.

    

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2. Deep reinforcement learning framework for controlling infectious disease outbreaks in the context of multi-jurisdictions

   https://doi.org/10.3934/mbe.2023640  

   Khatami, Seyedeh N. ; Gopalappa, Chaitra ( June 2023 , Mathematical Biosciences and Engineering)

   In the absence of pharmaceutical interventions, social distancing and lockdown have been key options for controlling new or reemerging respiratory infectious disease outbreaks. The timely implementation of these interventions is vital for effectively controlling and safeguarding the economy.Motivated by the COVID-19 pandemic, we evaluated whether, when, and to what level lockdowns are necessary to minimize epidemic and economic burdens of new disease outbreaks. We formulated the question as a sequential decision-making Markov Decision Process and solved it using deep Q-network algorithm. We evaluated the question under two objective functions: a 2-objective function to minimize economic burden and hospital capacity violations, suitable for diseases with severe health risks but with minimal death, and a 3-objective function that additionally minimizes the number of deaths, suitable for diseases that have high risk of mortality.A key feature of the model is that we evaluated the above questions in the context of two-geographical jurisdictions that interact through travel but make autonomous and independent decisions, evaluating under cross-jurisdictional cooperation and non-cooperation. In the 2-objective function under cross-jurisdictional cooperation, the optimal policy was to aim for shutdowns at 50 and 25% per day. Though this policy avoided hospital capacity violations, the shutdowns extended until a large proportion of the population reached herd immunity. Delays in initiating this optimal policy or non-cooperation from an outside jurisdiction required shutdowns at a higher level of 75% per day, thus adding to economic burdens. In the 3-objective function, the optimal policy under cross-jurisdictional cooperation was to aim for shutdowns of up to 75% per day to prevent deaths by reducing infected cases. This optimal policy continued for the entire duration of the simulation, suggesting that, until pharmaceutical interventions such as treatment or vaccines become available, contact reductions through physical distancing would be necessary to minimize deaths. Deviating from this policy increased the number of shutdowns and led to several deaths.In summary, we present a decision-analytic methodology for identifying optimal lockdown strategy under the context of interactions between jurisdictions that make autonomous and independent decisions. The numerical analysis outcomes are intuitive and, as expected, serve as proof of the feasibility of such a model. Our sensitivity analysis demonstrates that the optimal policy exhibits robustness to minor alterations in the transmission rate, yet shows sensitivity to more substantial deviations. This finding underscores the dynamic nature of epidemic parameters, thereby emphasizing the necessity for models trained across a diverse range of values to ensure effective policy-making. 

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3. A simulated natural heatwave perturbs bumblebee immunity and resistance to infection

   https://doi.org/10.1111/1365-2656.14041  

   Tobin, Kerrigan B. ; Mandes, Rachel ; Martinez, Abraham ; Sadd, Ben M. ( January 2024 , Journal of Animal Ecology)

   As a consequence of ongoing climate change, heatwaves are predicted to increase in frequency, intensity, and duration in many regions. Such extreme events can shift organisms from thermal optima for physiology and behaviour, with the thermal stress hypothesis predicting reduced performance at temperatures where the maintenance of biological functions is energetically costly. Performance includes the ability to resist biotic stressors, including infectious diseases, with increased exposure to extreme temperatures having the potential to synergise with parasite infection.

   Climate change is a proposed threat to native bee pollinators, directly and through indirect effects on floral resources, but the thermal stress hypothesis, particularly pertaining to infectious disease resistance, has received limited attention. We exposed adultBombus impatiensbumblebee workers to simulated, ecologically relevant heatwave or control thermal regimes and assessed longevity, immunity, and resistance to concurrent or future parasite infections.

   We demonstrate that survival and induced antibacterial immunity are reduced following heatwaves. Supporting that heatwave exposure compromised immunity, the cost of immune activation was thermal regime dependent, with survival costs in control but not heatwave exposed bees. However, in the face of real infections, an inability to mount an optimal immune response will be detrimental, which was reflected by increased trypanosomatid parasite infections following heatwave exposure.

   These results demonstrate interactions between heatwave exposure and bumblebee performance, including immune and infection outcomes. Thus, the health of bumblebee pollinator populations may be affected through altered interactions with parasites and pathogens, in addition to other effects of extreme manifestations of climate change.

    

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4. Reframing Optimal Control Problems for Infectious Disease Management in Low-Income Countries

   https://doi.org/10.1007/s11538-023-01137-4  

   Igoe, Morganne ; Casagrandi, Renato ; Gatto, Marino ; Hoover, Christopher M. ; Mari, Lorenzo ; Ngonghala, Calistus N. ; Remais, Justin V. ; Sanchirico, James N. ; Sokolow, Susanne H. ; Lenhart, Suzanne ; et al ( April 2023 , Bulletin of Mathematical Biology)

   Abstract Optimal control theory can be a useful tool to identify the best strategies for the management of infectious diseases. In most of the applications to disease control with ordinary differential equations, the objective functional to be optimized is formulated in monetary terms as the sum of intervention costs and the cost associated with the burden of disease. We present alternate formulations that express epidemiological outcomes via health metrics and reframe the problem to include features such as budget constraints and epidemiological targets. These alternate formulations are illustrated with a compartmental cholera model. The alternate formulations permit us to better explore the sensitivity of the optimal control solutions to changes in available budget or the desired epidemiological target. We also discuss some limitations of comprehensive cost assessment in epidemiology. 

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5. Human-network regions as effective geographic units for disease mitigation

   https://doi.org/10.1140/epjds/s13688-023-00426-1  

   Andris, Clio ; Koylu, Caglar ; Porter, Mason A. ( December 2023 , EPJ Data Science)

   Susceptibility to infectious diseases such as COVID-19 depends on how those diseases spread. Many studies have examined the decrease in COVID-19 spread due to reduction in travel. However, less is known about how much functional geographic regions, which capture natural movements and social interactions, limit the spread of COVID-19. To determine boundaries between functional regions, we apply community-detection algorithms to large networks of mobility and social-media connections to construct geographic regions that reflect natural human movement and relationships at the county level in the coterminous United States. We measure COVID-19 case counts, case rates, and case-rate variations across adjacent counties and examine how often COVID-19 crosses the boundaries of these functional regions. We find that regions that we construct using GPS-trace networks and especially commute networks have the lowest COVID-19 case rates along the boundaries, so these regions may reflect natural partitions in COVID-19 transmission. Conversely, regions that we construct from geolocated Facebook friendships and Twitter connections yield less effective partitions. Our analysis reveals that regions that are derived from movement flows are more appropriate geographic units than states for making policy decisions about opening areas for activity, assessing vulnerability of populations, and allocating resources. Our insights are also relevant for policy decisions and public messaging in future emergency situations.

    

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