No abstract

Models of metapopulations have focused on the effects of extinction and colonization rate upon metapopulation persistence and dynamics, assuming static landscapes wherein patches are neither created nor go extinct. However, for species living in ephemeral (patchy) habitats, landscapes are highly dynamic rather than static. In this article, we develop a lattice metapopulation model, of the patch occupancy type, based on interacting particle systems that incorporate explicitly both metapopulation and patch dynamics. Under this scenario, we study the effects of different regimes of patch dynamics upon metapopulation persistence. We analyze the lattice behavior by numerical simulations and a mean field approximation (MF). We show that metapopulation persistence and extinction are strongly influenced by the rate at which the landscape changes, in addition to the amount of habitat destroyed. We derive MF analytical expressions for extinction thresholds related to landscape properties such as habitat suitability and patch average lifetime. Using numerical simulations, we also show how these thresholds are quantitatively overestimated by the MF equations, although the qualitative behavior of the spatial model is well explained by the MF when the array of habitat patches is dynamic or static but connected in space and time. The implications for conservation are also discussed.

COVID-19 pandemic has underlined the impact of emergent pathogens as a major threat for human health. The development of quantitative approaches to advance comprehension of the current outbreak is urgently needed to tackle this severe disease. Considering different starting times of infection, mathematical models are proposed to represent SARS-CoV-2 dynamics in infected patients. Based on the target cell limited model, the within-host reproductive number for SARS-CoV-2 is consistent with the broad values of human influenza infection. The best model to fit the data was including immune cell response, which suggests a slow immune response peaking between 5 to 10 days post-onset of symptoms. The model with the eclipse phase, time in a latent phase before becoming productively infected cells, was not supported. Interestingly, all models predict that SARS-CoV-2 may replicate very slowly in the first days after infection, and viral load could be below detection levels during the first 4 days post infection. A quantitative comprehension of SARS-CoV-2 dynamics and the estimation of standard parameters of viral infections is the key contribution of this pioneering work. These models can serve for future evaluation of control theoretical approaches to tailor new potential drugs against COVID-19.

Arguably one of the most important effects of climate change is the potential impact on human health. While this is likely to take many forms, the implications for future transmission of vector-borne diseases (VBDs), given their ongoing contribution to global disease burden, are both extremely important and highly uncertain. In part, this is owing not only to data limitations and methodological challenges when integrating climate-driven VBD models and climate change projections, but also, perhaps most crucially, to the multitude of epidemiological, ecological and socio-economic factors that drive VBD transmission, and this complexity has generated considerable debate over the past 10–15 years. In this review, we seek to elucidate current knowledge around this topic, identify key themes and uncertainties, evaluate ongoing challenges and open research questions and, crucially, offer some solutions for the field. Although many of these challenges are ubiquitous across multiple VBDs, more specific issues also arise in different vector–pathogen systems.

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