Background Plasmodium vivax is a widely distributed, neglected parasite that can cause malaria and death in tropical areas. It is associated with an estimated 80–300 million cases of malaria worldwide. Brazilian tropical rain forests encompass host- and vector-rich communities, in which two hypothetical mechanisms could play a role in the dynamics of malaria transmission. The first mechanism is the dilution effect caused by presence of wild warm-blooded animals, which can act as dead-end hosts to Plasmodium parasites. The second is diffuse mosquito vector competition, in which vector and non-vector mosquito species compete for blood feeding upon a defensive host. Considering that the World Health Organization Malaria Eradication Research Agenda calls for novel strategies to eliminate malaria transmission locally, we used mathematical modeling to assess those two mechanisms in a pristine tropical rain forest, where the primary vector is present but malaria is absent.Methodology/Principal FindingsThe Ross–Macdonald model and a biodiversity-oriented model were parameterized using newly collected data and data from the literature. The basic reproduction number () estimated employing Ross–Macdonald model indicated that malaria cases occur in the study location. However, no malaria cases have been reported since 1980. In contrast, the biodiversity-oriented model corroborated the absence of malaria transmission. In addition, the diffuse competition mechanism was negatively correlated with the risk of malaria transmission, which suggests a protective effect provided by the forest ecosystem. There is a non-linear, unimodal correlation between the mechanism of dead-end transmission of parasites and the risk of malaria transmission, suggesting a protective effect only under certain circumstances (e.g., a high abundance of wild warm-blooded animals).Conclusions/SignificanceTo achieve biological conservation and to eliminate Plasmodium parasites in human populations, the World Health Organization Malaria Eradication Research Agenda should take biodiversity issues into consideration.

The variant of concern (VOC) P.1 emerged in the Amazonas state (Brazil) and was sequenced for the 1st time on 6-Jan-2021 by the Japanese National Institute of Infectious Diseases. It contains a constellation of mutations, ten of them in the spike protein. Consequences of these mutations at the populational level have been poorly studied so far. From December-2020 to February-2021, Manaus was devastated by four times more cases compared to the previous peak (April-2020). Here, data from the national health surveillance of hospitalized individuals were analysed using a model-based approach to estimate P.1 parameters of transmissibility and reinfection by maximum likelihood. Sensitivity analysis was performed changing pathogenicity and the period analysed (including/excluding the health system collapse period). In all analysed cases, the new variant transmissibility was found to be about 2.5 times higher compared to the previous variant in Manaus. A low probability of reinfection by the new variant (6.4%) was estimated, even under initial high prevalence (68%) by the time P.1 emerged. Consequences of a higher transmissibility were already observed with VOC B.1.1.7 in the UK and Europe. Urgent measures must be taken to control the spread of P.1.

Understanding how temperature influences population regulation through its effects on intraspecific competition is an important question for which there is currently little theory or data. Here we develop a theoretical framework for elucidating temperature effects on competition that integrates mechanistic descriptions of life-history trait responses to temperature with population models that realistically capture the variable developmental delays that characterize ectotherm life cycles. This framework yields testable comparative predictions about how intraspecific competition affects reproduction, development, and mortality under alternative hypotheses about the temperature dependence of competition. The key finding is that ectotherm population regulation in seasonal environments depends crucially on the mechanisms by which temperature affects competition. When competition is strongest at temperatures optimal for reproduction, effects of temperature and competition act antagonistically, leading to more complex dynamics than when competition is temperature independent. When the strength of competition increases with temperature past the optimal temperature for reproduction, effects of temperature and competition act synergistically, leading to dynamics qualitatively similar to those when competition is temperature independent. Paradoxically, antagonistic effects yield a higher population floor despite greater fluctuations. These findings have important implications for predicting effects of climate warming on population regulation. Synergistic effects of temperature and competition can predispose populations to stochastic extinction by lowering minimum population sizes, while antagonistic effects can increase the potential for population outbreaks through greater fluctuations in abundance.

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The SARS-CoV-2 pandemic has had an unprecedented impact on multiple levels of society. Not only has the pandemic completely overwhelmed some health systems but it has also changed how scientific evidence is shared and increased the pace at which such evidence is published and consumed, by scientists, policymakers and the wider public. More significantly, the pandemic has created tremendous challenges for decision-makers, who have had to implement highly disruptive containment measures with very little empirical scientific evidence to support their decision-making process. Given this lack of data, predictive mathematical models have played an increasingly prominent role. In high-income countries, there is a long-standing history of established research groups advising policymakers, whereas a general lack of translational capacity has meant that mathematical models frequently remain inaccessible to policymakers in low-income and middle-income countries. Here, we describe a participatory approach to modelling that aims to circumvent this gap. Our approach involved the creation of an international group of infectious disease modellers and other public health experts, which culminated in the establishment of the COVID-19 Modelling (CoMo) Consortium. Here, we describe how the consortium was formed, the way it functions, the mathematical model used and, crucially, the high degree of engagement fostered between CoMo Consortium members and their respective local policymakers and ministries of health.

Summary In species with complex life cycles, population dynamics result from a combination of intrinsic cycles arising from delays in the operation of negative density-dependent processes (e.g., intraspecific competition) and extrinsic fluctuations arising from seasonal variation in the abiotic environment. Abiotic variation can affect species directly through their life history traits and indirectly by modulating the species’ interactions with resources or natural enemies.We investigate how the interplay between density-dependent dynamics and abiotic variability affects population dynamics of the bordered plant bug (Largus californicus), a Hemipteran herbivore inhabiting the California coastal sage scrub community. Field data show a striking pattern in abundance: adults are extremely abundant or nearly absent during certain periods of the year, leading us to predict that seasonal forcing plays a role in driving observed dynamics.We develop a stage-structured population model with variable developmental delays, in which fecundity is affected by both intra-specific competition and temporal variation in resource availability and all life history traits (reproduction, development, mortality) are temperature-dependent. We parameterize the model with experimental data on temperature-responses of life history and competitive traits and validate the model with independent field census data.We find that intra-specific competition is strongest at temperatures optimal for reproduction, which theory predicts leads to more complex population dynamics. Our model predicts that while temperature or resource variability interact with development-induced delays in self-limitation to generate population fluctuations, it is the interplay between all three factors that drive the observed dynamics. Considering how multiple abiotic factors interact with density-dependent processes is important both for understanding how species persist in variable environments and predicting species’ responses to perturbations in their typical environment.

Background The SARS-CoV-2 variant of concern (VOC) P.1 (Gamma variant) emerged in the Amazonas State, Brazil, in November 2020. The epidemiological consequences of its mutations have not been widely studied, despite detection of P.1 in 36 countries, with local transmission in at least 5 countries. A range of mutations are seen in P.1, ten of them in the spike protein. It shares mutations with VOCs previously detected in the United Kingdom (B.1.1.7, Alpha variant) and South Africa (B.1.351, Beta variant). Methods We estimated the transmissibility and reinfection of P.1 using a model-based approach, fitting data from the national health surveillance of hospitalized individuals and frequency of the P.1 variant in Manaus from December-2020 to February-2021. Results Here we estimate that the new variant is about 2.6 times more transmissible (95% Confidence Interval: 2.4–2.8) than previous circulating variant(s). Manaus already had a high prevalence of individuals previously affected by the SARS-CoV-2 virus and our fitted model attributed 28% of Manaus cases in the period to reinfections by P.1, confirming the importance of reinfection by this variant. This value is in line with estimates from blood donors samples in Manaus city. Conclusions Our estimates rank P.1 as one of the most transmissible among the SARS-CoV-2 VOCs currently identified, and potentially as transmissible as the posteriorly detected VOC B.1.617.2 (Delta variant), posing a serious threat and requiring measures to control its global spread.

Dexamethasone can reduce mortality in hospitalised COVID-19 patients needing oxygen and ventilation by 18% and 36%, respectively. Here, we estimate the potential number of lives saved and life years gained if this treatment were to be rolled out in the UK and globally, as well as the cost-effectiveness of implementing this intervention. Assuming SARS-CoV-2 exposure levels of 5% to 15%, we estimate that, for the UK, approximately 12,000 (4,250 - 27,000) lives could be saved between July and December 2020. Assuming that dexamethasone has a similar effect size in settings where access to oxygen therapies is limited, this would translate into approximately 650,000 (240,000 - 1,400,000) lives saved globally over the same time period. If dexamethasone acts differently in these settings, the impact could be less than half of this value. To estimate the full potential of dexamethasone in the global fight against COVID-19, it is essential to perform clinical research in settings with limited access to oxygen and/or ventilators, for example in low- and middle-income countries.