ABSTRACT
Background: COVID-19 has several overlapping phases. Treatments to date have focused on the late stage of disease in hospital. Yet, the pandemic is by propagated by the viral phase in out-patients. The current public health strategy relies solely on vaccines to prevent disease.Methods: We searched the major national registries, pubmed.org, and the preprint servers for all ongoing, completed and published trial results.Results: As of 2/15/2021, we found 111 publications reporting findings on 14 classes of agents, and 9 vaccines. There were 62 randomized controlled studies, the rest retrospective observational analyses. Only 21 publications dealt with outpatient care. Remdesivir and high titer convalescent plasma have emergency use authorization for hospitalized patients in the U.S.A. There is also support for glucocorticoid treatment of the COVID-19 respiratory distress syndrome. Monoclonal antibodies are authorized for outpatients, but supply is inadequate to treat all at time of diagnosis. Favipiravir, ivermectin, and interferons are approved in certain countries.Expert Opinion: Vaccines and antibodies are highly antigen specific, and new SARS-Cov-2 variants are appearing. We call on public health authorities to authorize treatments with known low-risk and possible benefit for outpatients in parallel with universal vaccination.
Subject(s)
COVID-19 Vaccines/administration & dosage , COVID-19/therapy , Ambulatory Care/methods , Antibodies, Monoclonal/administration & dosage , COVID-19/diagnosis , COVID-19/prevention & control , Hospitalization , Humans , Immunization, Passive , Randomized Controlled Trials as Topic , Time Factors , COVID-19 Drug Treatment , COVID-19 SerotherapyABSTRACT
We combine COVID-19 case data with mobility data to estimate a modified susceptible-infected-recovered (SIR) model in the United States. In contrast to a standard SIR model, we find that the incidence of COVID-19 spread is concave in the number of infectious individuals, as would be expected if people have inter-related social networks. This concave shape has a significant impact on forecasted COVID-19 cases. In particular, our model forecasts that the number of COVID-19 cases would only have an exponential growth for a brief period at the beginning of the contagion event or right after a reopening, but would quickly settle into a prolonged period of time with stable, slightly declining levels of disease spread. This pattern is consistent with observed levels of COVID-19 cases in the US, but inconsistent with standard SIR modeling. We forecast rates of new cases for COVID-19 under different social distancing norms and find that if social distancing is eliminated there will be a massive increase in the cases of COVID-19.