A global systematic analysis of the occurrence, severity, and recovery pattern of long COVID in 2020 and 2021 (preprint)

ImportanceWhile much of the attention on the COVID-19 pandemic was directed at the daily counts of cases and those with serious disease overwhelming health services, increasingly, reports have appeared of people who experience debilitating symptoms after the initial infection. This is popularly known as long COVID. ObjectiveTo estimate by country and territory of the number of patients affected by long COVID in 2020 and 2021, the severity of their symptoms and expected pattern of recovery DesignWe jointly analyzed ten ongoing cohort studies in ten countries for the occurrence of three major symptom clusters of long COVID among representative COVID cases. The defining symptoms of the three clusters (fatigue, cognitive problems, and shortness of breath) are explicitly mentioned in the WHO clinical case definition. For incidence of long COVID, we adopted the minimum duration after infection of three months from the WHO case definition. We pooled data from the contributing studies, two large medical record databases in the United States, and findings from 44 published studies using a Bayesian meta-regression tool. We separately estimated occurrence and pattern of recovery in patients with milder acute infections and those hospitalized. We estimated the incidence and prevalence of long COVID globally and by country in 2020 and 2021 as well as the severity-weighted prevalence using disability weights from the Global Burden of Disease study. ResultsAnalyses are based on detailed information for 1906 community infections and 10526 hospitalized patients from the ten collaborating cohorts, three of which included children. We added published data on 37262 community infections and 9540 hospitalized patients as well as ICD-coded medical record data concerning 1.3 million infections. Globally, in 2020 and 2021, 144.7 million (95% uncertainty interval [UI] 54.8-312.9) people suffered from any of the three symptom clusters of long COVID. This corresponds to 3.69% (1.38-7.96) of all infections. The fatigue, respiratory, and cognitive clusters occurred in 51.0% (16.9-92.4), 60.4% (18.9-89.1), and 35.4% (9.4-75.1) of long COVID cases, respectively. Those with milder acute COVID-19 cases had a quicker estimated recovery (median duration 3.99 months [IQR 3.84-4.20]) than those admitted for the acute infection (median duration 8.84 months [IQR 8.10-9.78]). At twelve months, 15.1% (10.3-21.1) continued to experience long COVID symptoms. Conclusions and relevanceThe occurrence of debilitating ongoing symptoms of COVID-19 is common. Knowing how many people are affected, and for how long, is important to plan for rehabilitative services and support to return to social activities, places of learning, and the workplace when symptoms start to wane. Key PointsO_ST_ABSQuestionC_ST_ABSWhat are the extent and nature of the most common long COVID symptoms by country in 2020 and 2021? FindingsGlobally, 144.7 million people experienced one or more of three symptom clusters (fatigue; cognitive problems; and ongoing respiratory problems) of long COVID three months after infection, in 2020 and 2021. Most cases arose from milder infections. At 12 months after infection, 15.1% of these cases had not yet recovered. MeaningThe substantial number of people with long COVID are in need of rehabilitative care and support to transition back into the workplace or education when symptoms start to wane.

COVID-19 scenarios for the United States (preprint)

The United States (US) has not been spared in the ongoing pandemic of novel coronavirus disease1,2. COVID-19, caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), continues to cause death and disease in all 50 states, as well as significant economic damage wrought by the non-pharmaceutical interventions (NPI) adopted in attempts to control transmission3. We use a deterministic, Susceptible, Exposed, Infectious, Recovered (SEIR) compartmental framework4,5 to model possible trajectories of SARS-CoV-2 infections and the impact of NPI6 at the state level. Model performance was tested against reported deaths from 01 February to 04 July 2020. Using this SEIR model and projections of critical driving covariates (pneumonia seasonality, mobility, testing rates, and mask use per capita), we assessed some possible futures of the COVID-19 pandemic from 05 July through 31 December 2020. We explored future scenarios that included feasible assumptions about NPIs including social distancing mandates (SDMs) and levels of mask use. The range of infection, death, and hospital demand outcomes revealed by these scenarios show that action taken during the summer of 2020 will have profound public health impacts through to the year end. Encouragingly, we find that an emphasis on universal mask use may be sufficient to ameliorate the worst effects of epidemic resurgences in many states. Masks may save as many as 102,795 (55,898-183,374) lives, when compared to a plausible reference scenario in December. In addition, widespread mask use may markedly reduce the need for more socially and economically deleterious SDMs.

Predictive performance of international COVID-19 mortality forecasting models (preprint)

Background: Forecasts and alternative scenarios of the COVID-19 pandemic have been critical inputs into a range of important decisions by healthcare providers, local and national government agencies and international organizations and actors. Hundreds of COVID-19 models have been released. Decision-makers need information about the predictive performance of these models to help select which ones should be used to guide decision-making. Methods: We identified 383 published or publicly released COVID-19 forecasting models. Only seven models met the inclusion criteria of: estimating for five or more countries, providing regular updates, forecasting at least 4 weeks from the model release date, estimating mortality, and providing date-versioned sets of previously estimated forecasts. These models included those produced by: DELPHI-MIT (Delphi), Youyang Gu (YYG), the Los Alamos National Laboratory (LANL), Imperial College London (Imperial), and three models produced by the Institute for Health Metrics and Evaluation (IHME). For each of these models, we examined the median absolute percent error-compared to subsequently observed trends-for weekly and cumulative death forecasts. Errors were stratified by weeks of extrapolation, world region, and month of model estimation. For locations with epidemics showing a clear peak, each model's accuracy was also evaluated in predicting the timing of peak daily mortality. Results: Across models, the median absolute percent error (MAPE) on cumulative deaths for models released in June rose with increased weeks of extrapolation, from 2.3% at one week to 16.3% at six weeks. MAPE at 6 weeks was less than 20% for the IHME-MS-SEIR model (10.2%), YYG (11.1%), LANL (12.6%) and Delphi (19.1%). Across models, MAPE at six weeks were the highest in Sub-Saharan Africa (65.3%), and the lowest in high-income countries (8.5%). Median absolute errors (MAE) for peak timing also rose with increased forecasting weeks, from 9 days at one week to 30 days at six weeks. Peak timing MAE at six weeks ranged from 20 days for the IHME Curve Fit model, to 38 days for the LANL model. Interpretation: Four of the models, from IHME, YYG, Delphi and LANL, had less than 20% MAPE at six weeks. Despite the complexities of modelling human behavioural responses and government interventions related to COVID-19, predictions among these better-performing models were surprisingly accurate. Forecasts and alternative scenarios can be a useful input to decision-makers, although users should be aware of increasing errors with a greater amount of extrapolation time, and corresponding steadily widening uncertainty intervals further in the future. The framework and publicly available codebase presented can be routinely used to evaluate the performance of all publicly released models meeting inclusion criteria in the future, and compare current model predictions.