Time-restricted feeding attenuates hypercholesterolaemia and atherosclerosis development during circadian disturbance in APOE∗3-Leiden.CETP mice

Circadian disturbance (CD) is the consequence of a mismatch between endogenous circadian rhythms, behaviour, and/or environmental cycles, and frequently occurs during shift work. Shift work has been associated with elevated risk for atherosclerotic cardiovascular disease (asCVD) in humans, but evidence for the effectiveness of prevention strategies is lacking.\nHere, we applied time-restricted feeding (TRF) as a strategy to counteract atherosclerosis development during CD in female APOE∗3-Leiden.CETP mice, a well-established model for humanized lipoprotein metabolism. Control groups were subjected to a fixed 12:12 h light-dark cycle, while CD groups were subjected to 6-h phase advancement every 3 days. Groups had either ad libitum (AL) access to food or were subjected to TRF with restricted food access to the dark phase.\nTRF did not prevent the increase in the relative abundance of circulating inflammatory monocytes and elevation of (postprandial) plasma triglycerides during CD. Nonetheless, TRF reduced atherosclerotic lesion size and prevented an elevation in macrophage content of atherosclerotic lesions during CD, while it increased the relative abundance of anti-inflammatory monocytes, prevented activation of T cells, and lowered plasma total cholesterol levels and markers of hepatic cholesterol synthesis. These effects were independent of total food intake.\nWe propose that time restricted eating could be a promising strategy for the primary prevention of asCVD risk in shift workers, which warrants future study in humans.\nThis work was funded by the Novo Nordisk Foundation, the Netherlands Ministry of Social Affairs and Employment, Amsterdam Cardiovascular Sciences, and the Dutch Heart Foundation.Biopharmaceutic

The US COVID-19 and Influenza Scenario Modeling Hubs: Delivering long-term projections to guide policy

Between December 2020 and April 2023, the COVID-19 Scenario Modeling Hub (SMH) generated operational multi-month projections of COVID-19 burden in the US to guide pandemic planning and decision-making in the context of high uncertainty. This effort was born out of an attempt to coordinate, synthesize and effectively use the unprecedented amount of predictive modeling that emerged throughout the COVID-19 pandemic. Here we describe the history of this massive collective research effort, the process of convening and maintaining an open modeling hub active over multiple years, and attempt to provide a blueprint for future efforts. We detail the process of generating 17 rounds of scenarios and projections at different stages of the COVID-19 pandemic, and disseminating results to the public health community and lay public. We also highlight how SMH was expanded to generate influenza projections during the 2022–23 season. We identify key impacts of SMH results on public health and draw lessons to improve future collaborative modeling efforts, research on scenario projections, and the interface between models and policy

Evaluation of the US COVID-19 Scenario Modeling Hub for informing pandemic response under uncertainty

Our ability to forecast epidemics far into the future is constrained by the many complexities of disease systems. Realistic longer-term projections may, however, be possible under well-defined scenarios that specify the future state of critical epidemic drivers. Since December 2020, the U.S. COVID-19 Scenario Modeling Hub (SMH) has convened multiple modeling teams to make months ahead projections of SARS-CoV-2 burden, totaling nearly 1.8 million national and state-level projections. Here, we find SMH performance varied widely as a function of both scenario validity and model calibration. We show scenarios remained close to reality for 22 weeks on average before the arrival of unanticipated SARS-CoV-2 variants invalidated key assumptions. An ensemble of participating models that preserved variation between models (using the linear opinion pool method) was consistently more reliable than any single model in periods of valid scenario assumptions, while projection interval coverage was near target levels. SMH projections were used to guide pandemic response, illustrating the value of collaborative hubs for longer-term scenario projections

Region-wide synchrony and traveling waves of dengue across eight countries in Southeast Asia [plus Supporting information]

Dengue is a mosquito-transmitted virus infection that causes epidemics of febrile illness and hemorrhagic fever across the tropics and subtropics worldwide. Annual epidemics are commonly observed, but there is substantial spatiotemporal heterogeneity in intensity. A better understanding of this heterogeneity in dengue transmission could lead to improved epidemic prediction and disease control. Time series decomposition methods enable the isolation and study of temporal epidemic dynamics with a specific periodicity (e.g., annual cycles related to climatic drivers and multiannual cycles caused by dynamics in population immunity). We collected and analyzed up to 18 y of monthly dengue surveillance reports on a total of 3.5 million reported dengue cases from 273 provinces in eight countries in Southeast Asia, covering similar to 10(7) km(2). We detected strong patterns of synchronous dengue transmission across the entire region, most markedly during a period of high incidence in 1997-1998, which was followed by a period of extremely low incidence in 2001-2002. This synchrony in dengue incidence coincided with elevated temperatures throughout the region in 1997-1998 and the strongest El NiNo episode of the century. Multiannual dengue cycles (2-5 y) were highly coherent with the Oceanic NiNo Index, and synchrony of these cycles increased with temperature. We also detected localized traveling waves of multiannual dengue epidemic cycles in Thailand, Laos, and the Philippines that were dependent on temperature. This study reveals forcing mechanisms that drive synchronization of dengue epidemics on a continental scale across Southeast Asia

Region-wide synchrony and traveling waves of dengue across eight countries in Southeast Asia [plus Supporting information]

Dengue is a mosquito-transmitted virus infection that causes epidemics of febrile illness and hemorrhagic fever across the tropics and subtropics worldwide. Annual epidemics are commonly observed, but there is substantial spatiotemporal heterogeneity in intensity. A better understanding of this heterogeneity in dengue transmission could lead to improved epidemic prediction and disease control. Time series decomposition methods enable the isolation and study of temporal epidemic dynamics with a specific periodicity (e.g., annual cycles related to climatic drivers and multiannual cycles caused by dynamics in population immunity). We collected and analyzed up to 18 y of monthly dengue surveillance reports on a total of 3.5 million reported dengue cases from 273 provinces in eight countries in Southeast Asia, covering similar to 10(7) km(2). We detected strong patterns of synchronous dengue transmission across the entire region, most markedly during a period of high incidence in 1997-1998, which was followed by a period of extremely low incidence in 2001-2002. This synchrony in dengue incidence coincided with elevated temperatures throughout the region in 1997-1998 and the strongest El NiNo episode of the century. Multiannual dengue cycles (2-5 y) were highly coherent with the Oceanic NiNo Index, and synchrony of these cycles increased with temperature. We also detected localized traveling waves of multiannual dengue epidemic cycles in Thailand, Laos, and the Philippines that were dependent on temperature. This study reveals forcing mechanisms that drive synchronization of dengue epidemics on a continental scale across Southeast Asia