Krarupbossen8660

Background As COVID-19 continues to spread around the world, understanding how patterns of human mobility and connectivity affect outbreak dynamics, especially before outbreaks establish locally, is critical for informing response efforts. In Taiwan, most cases to date were imported or linked to imported cases. Methods In collaboration with Facebook Data for Good, we characterized changes in movement patterns in Taiwan since February 2020, and built metapopulation models that incorporate human movement data to identify the high risk areas of disease spread and assess the potential effects of local travel restrictions in Taiwan. Results We found that mobility changed with the number of local cases in Taiwan in the past few months. For each city, we identified the most highly connected areas that may serve as sources of importation during an outbreak. We showed that the risk of an outbreak in Taiwan is enhanced if initial infections occur around holidays. Intracity travel reductions have a higher impact on the risk of an outbreak than intercity travel reductions, while intercity travel reductions can narrow the scope of the outbreak and help target resources. The timing, duration, and level of travel reduction together determine the impact of travel reductions on the number of infections, and multiple combinations of these can result in similar impact. Conclusions To prepare for the potential spread within Taiwan, we utilized Facebook's aggregated and anonymized movement and colocation data to identify cities with higher risk of infection and regional importation. We developed an interactive application that allows users to vary inputs and assumptions and shows the spatial spread of the disease and the impact of intercity and intracity travel reduction under different initial conditions. Our results can be used readily if local transmission occurs in Taiwan after relaxation of border control, providing important insights into future disease surveillance and policies for travel restrictions.Many colleges and other organizations are considering testing plans to return to operation as the COVID19 pandemic continues. The temporal dynamics of viral load and test false negative rate have the potential to alter the apparent efficacy of testing, as testing must identify a sick individual prior to that person transmitting the virus to one or more people and isolate them. High levels of pre-symptomatic spread and high false negative rates for testing would therefore be likely to make it difficult to successfully test an individual in the time frame necessary to stop viral spread. Here, we develop a stochastic agent-based model of COVID19 in a university sized population, considering the dynamics of both viral load and false negative rate of tests on the ability of testing to combat viral spread. We find that the undetectable period of SARS-CoV-2 can lead to an apparent false negative rate of ~17% in the presence of a hypothetical perfect test, while full implementation of dynamic false negative rates reported in the literature leads to an overall false negative rate of ~48%. We then compare testing while varying fraction of the population and the frequency of testing. We find that these assumptions about viral load and false negative rate lead to a requirement for high levels of both frequency and fraction of population tested in order to bring the apparent R0 below 1. We conclude that models that do not consider the non-uniform dynamics of viral spread and false negative rate may come up with unrealistic testing plans that will not lead to the desired reduction in apparent R0.The COVID-19 pandemic has brought to the fore the need for policy makers to receive timely and ongoing scientific guidance in response to this recently emerged human infectious disease. find more Fitting mathematical models of infectious disease transmission to the available epidemiological data provides a key statistical tool for understanding the many quantities of interest that are not explicit in the underlying epidemiological data streams. Of these, the basic reproductive ratio, R, has taken on special significance in terms of the general understanding of whether the epidemic is under control (R less then 1). Unfortunately, none of the epidemiological data streams are designed for modelling, hence assimilating information from multiple (often changing) sources of data is a major challenge that is particularly stark in novel disease outbreaks. Here, we present in some detail the inference scheme employed for calibrating the Warwick COVID-19 model to the available public health data streams, which span hospitalisations, critical care occupancy, mortality and serological testing. We then perform computational simulations, making use of the acquired parameter posterior distributions, to assess how the accuracy of short-term predictions varied over the timecourse of the outbreak. To conclude, we compare how refinements to data streams and model structure impact estimates of epidemiological measures, including the estimated growth rate and daily incidence.We conducted an extensive serological study to quantify population-level exposure and define correlates of immunity against SARS-CoV-2. We found that relative to mild COVID-19 cases, individuals with severe disease exhibited elevated authentic virus-neutralizing titers and antibody levels against nucleocapsid (N) and the receptor binding domain (RBD) and the S2 region of spike protein. Unlike disease severity, age and sex played lesser roles in serological responses. All cases, including asymptomatic individuals, seroconverted by 2 weeks post-PCR confirmation. RBD- and S2-specific and neutralizing antibody titers remained elevated and stable for at least 2-3 months post-onset, whereas those against N were more variable with rapid declines in many samples. Testing of 5882 self-recruited members of the local community demonstrated that 1.24% of individuals showed antibody reactivity to RBD. However, 18% (13/73) of these putative seropositive samples failed to neutralize authentic SARS-CoV-2 virus. Each of the neutralizing, but only 1 of the non-neutralizing samples, also displayed potent reactivity to S2. Thus, inclusion of multiple independent assays markedly improved the accuracy of antibody tests in low seroprevalence communities and revealed differences in antibody kinetics depending on the viral antigen. In contrast to other reports, we conclude that immunity is durable for at least several months after SARS-CoV-2 infection.