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1.
Cramer, Estee, Ray, Evan, Lopez, Velma, Bracher, Johannes, Brennen, Andrea, Castro§Rivadeneira, Alvaro, Gerding, Aaron, Gneiting, Tilmann, House, Katie, Huang, Yuxin, Jayawardena, Dasuni, Kanji, Abdul, Khandelwal, Ayush, Le, Khoa, Mühlemann, Anja, Niemi, Jarad, Shah, Apurv, Stark, Ariane, Wang, Yijin, Wattanachit, Nutcha, Zorn, Martha, Gu, Youyang, Jain, Sansiddh, Bannur, Nayana, Deva, Ayush, Kulkarni, Mihir, Merugu, Srujana, Raval, Alpan, Shingi, Siddhant, Tiwari, Avtansh, White, Jerome, Abernethy, Neil, Woody, Spencer, Dahan, Maytal, Fox, Spencer, Gaither, Kelly, Lachmann, Michael, Meyers, Lauren Ancel, Scott, James, Tec, Mauricio, Srivastava, Ajitesh, George, Glover, Cegan, Jeffrey, Dettwiller, Ian, England, William, Farthing, Matthew, Hunter, Robert, Lafferty, Brandon, Linkov, Igor, Mayo, Michael, Parno, Matthew, Rowland, Michael, Trump, Benjamin, Zhang-James, Yanli, Chen, Samuel, Faraone, Stephen, Hess, Jonathan, Morley, Christopher, Salekin, Asif, Wang, Dongliang, Corsetti, Sabrina, Baer, Thomas, Eisenberg, Marisa, Falb, Karl, Huang, Yitao, Martin, Emily, McCauley, Ella, Myers, Robert, Schwarz, Tom, Sheldon, Daniel, Gibson, Graham Casey, Yu, Rose, Gao, Liyao, Ma, Yian, Wu, Dongxia, Yan, Xifeng, Jin, Xiaoyong, Wang, Yu-Xiang, Chen, YangQuan, Guo, Lihong, Zhao, Yanting, Gu, Quanquan, Chen, Jinghui, Wang, Lingxiao, Xu, Pan, Zhang, Weitong, Zou, Difan, Biegel, Hannah, Lega, Joceline, McConnell, Steve, Nagraj, V. P.; Guertin, Stephanie, Hulme-Lowe, Christopher, Turner, Stephen, Shi, Yunfeng, Ban, Xuegang, Walraven, Robert, Hong, Qi-Jun, Kong, Stanley, van§de§Walle, Axel, Turtle, James, Ben-Nun, Michal, Riley, Steven, Riley, Pete, Koyluoglu, Ugur, DesRoches, David, Forli, Pedro, Hamory, Bruce, Kyriakides, Christina, Leis, Helen, Milliken, John, Moloney, Michael, Morgan, James, Nirgudkar, Ninad, Ozcan, Gokce, Piwonka, Noah, Ravi, Matt, Schrader, Chris, Shakhnovich, Elizabeth, Siegel, Daniel, Spatz, Ryan, Stiefeling, Chris, Wilkinson, Barrie, Wong, Alexander, Cavany, Sean, España, Guido, Moore, Sean, Oidtman, Rachel, Perkins, Alex, Kraus, David, Kraus, Andrea, Gao, Zhifeng, Bian, Jiang, Cao, Wei, Ferres, Juan Lavista, Li, Chaozhuo, Liu, Tie-Yan, Xie, Xing, Zhang, Shun, Zheng, Shun, Vespignani, Alessandro, Chinazzi, Matteo, Davis, Jessica, Mu, Kunpeng, y§Piontti, Ana Pastore, Xiong, Xinyue, Zheng, Andrew, Baek, Jackie, Farias, Vivek, Georgescu, Andreea, Levi, Retsef, Sinha, Deeksha, Wilde, Joshua, Perakis, Georgia, Bennouna, Mohammed Amine, Nze-Ndong, David, Singhvi, Divya, Spantidakis, Ioannis, Thayaparan, Leann, Tsiourvas, Asterios, Sarker, Arnab, Jadbabaie, Ali, Shah, Devavrat, Penna, Nicolas Della, Celi, Leo, Sundar, Saketh, Wolfinger, Russ, Osthus, Dave, Castro, Lauren, Fairchild, Geoffrey, Michaud, Isaac, Karlen, Dean, Kinsey, Matt, Mullany, Luke, Rainwater-Lovett, Kaitlin, Shin, Lauren, Tallaksen, Katharine, Wilson, Shelby, Lee, Elizabeth, Dent, Juan, Grantz, Kyra, Hill, Alison, Kaminsky, Joshua, Kaminsky, Kathryn, Keegan, Lindsay, Lauer, Stephen, Lemaitre, Joseph, Lessler, Justin, Meredith, Hannah, Perez-Saez, Javier, Shah, Sam, Smith, Claire, Truelove, Shaun, Wills, Josh, Marshall, Maximilian, Gardner, Lauren, Nixon, Kristen, Burant, John, Wang, Lily, Gao, Lei, Gu, Zhiling, Kim, Myungjin, Li, Xinyi, Wang, Guannan, Wang, Yueying, Yu, Shan, Reiner, Robert, Barber, Ryan, Gakidou, Emmanuela, Hay, Simon, Lim, Steve, Murray, Chris J. L.; Pigott, David, Gurung, Heidi, Baccam, Prasith, Stage, Steven, Suchoski, Bradley, Prakash, Aditya, Adhikari, Bijaya, Cui, Jiaming, Rodríguez, Alexander, Tabassum, Anika, Xie, Jiajia, Keskinocak, Pinar, Asplund, John, Baxter, Arden, Oruc, Buse Eylul, Serban, Nicoleta, Arik, Sercan, Dusenberry, Mike, Epshteyn, Arkady, Kanal, Elli, Le, Long, Li, Chun-Liang, Pfister, Tomas, Sava, Dario, Sinha, Rajarishi, Tsai, Thomas, Yoder, Nate, Yoon, Jinsung, Zhang, Leyou, Abbott, Sam, Bosse, Nikos, Funk, Sebastian, Hellewell, Joel, Meakin, Sophie, Sherratt, Katharine, Zhou, Mingyuan, Kalantari, Rahi, Yamana, Teresa, Pei, Sen, Shaman, Jeffrey, Li, Michael, Bertsimas, Dimitris, Lami, Omar Skali, Soni, Saksham, Bouardi, Hamza Tazi, Ayer, Turgay, Adee, Madeline.
EuropePMC; 2021.
Preprint in English | EuropePMC | ID: ppcovidwho-329225

ABSTRACT

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub ( https://covid19forecasthub.org/ ) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multi-model ensemble forecast that combined predictions from dozens of different research groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-week horizon 3-5 times larger than when predicting at a 1-week horizon. This project underscores the role that collaboration and active coordination between governmental public health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks. Significance Statement This paper compares the probabilistic accuracy of short-term forecasts of reported deaths due to COVID-19 during the first year and a half of the pandemic in the US. Results show high variation in accuracy between and within stand-alone models, and more consistent accuracy from an ensemble model that combined forecasts from all eligible models. This demonstrates that an ensemble model provided a reliable and comparatively accurate means of forecasting deaths during the COVID-19 pandemic that exceeded the performance of all of the models that contributed to it. This work strengthens the evidence base for synthesizing multiple models to support public health action.

2.
BMC Med ; 20(1): 86, 2022 02 21.
Article in English | MEDLINE | ID: covidwho-1703629

ABSTRACT

BACKGROUND: Forecasting healthcare demand is essential in epidemic settings, both to inform situational awareness and facilitate resource planning. Ideally, forecasts should be robust across time and locations. During the COVID-19 pandemic in England, it is an ongoing concern that demand for hospital care for COVID-19 patients in England will exceed available resources. METHODS: We made weekly forecasts of daily COVID-19 hospital admissions for National Health Service (NHS) Trusts in England between August 2020 and April 2021 using three disease-agnostic forecasting models: a mean ensemble of autoregressive time series models, a linear regression model with 7-day-lagged local cases as a predictor, and a scaled convolution of local cases and a delay distribution. We compared their point and probabilistic accuracy to a mean-ensemble of them all and to a simple baseline model of no change from the last day of admissions. We measured predictive performance using the weighted interval score (WIS) and considered how this changed in different scenarios (the length of the predictive horizon, the date on which the forecast was made, and by location), as well as how much admissions forecasts improved when future cases were known. RESULTS: All models outperformed the baseline in the majority of scenarios. Forecasting accuracy varied by forecast date and location, depending on the trajectory of the outbreak, and all individual models had instances where they were the top- or bottom-ranked model. Forecasts produced by the mean-ensemble were both the most accurate and most consistently accurate forecasts amongst all the models considered. Forecasting accuracy was improved when using future observed, rather than forecast, cases, especially at longer forecast horizons. CONCLUSIONS: Assuming no change in current admissions is rarely better than including at least a trend. Using confirmed COVID-19 cases as a predictor can improve admissions forecasts in some scenarios, but this is variable and depends on the ability to make consistently good case forecasts. However, ensemble forecasts can make forecasts that make consistently more accurate forecasts across time and locations. Given minimal requirements on data and computation, our admissions forecasting ensemble could be used to anticipate healthcare needs in future epidemic or pandemic settings.


Subject(s)
COVID-19 , Forecasting , Hospitals , Humans , Pandemics , SARS-CoV-2 , State Medicine
3.
Nat Commun ; 12(1): 5968, 2021 10 13.
Article in English | MEDLINE | ID: covidwho-1467102

ABSTRACT

There is conflicting evidence on the influence of weather on COVID-19 transmission. Our aim is to estimate weather-dependent signatures in the early phase of the pandemic, while controlling for socio-economic factors and non-pharmaceutical interventions. We identify a modest non-linear association between mean temperature and the effective reproduction number (Re) in 409 cities in 26 countries, with a decrease of 0.087 (95% CI: 0.025; 0.148) for a 10 °C increase. Early interventions have a greater effect on Re with a decrease of 0.285 (95% CI 0.223; 0.347) for a 5th - 95th percentile increase in the government response index. The variation in the effective reproduction number explained by government interventions is 6 times greater than for mean temperature. We find little evidence of meteorological conditions having influenced the early stages of local epidemics and conclude that population behaviour and government interventions are more important drivers of transmission.


Subject(s)
COVID-19/transmission , Meteorological Concepts , SARS-CoV-2/pathogenicity , Basic Reproduction Number , COVID-19/epidemiology , Cities , Cross-Sectional Studies , Humans , Meta-Analysis as Topic , Pandemics , Regression Analysis , Seasons , Temperature , Weather
4.
Philos Trans R Soc Lond B Biol Sci ; 376(1829): 20200283, 2021 07 19.
Article in English | MEDLINE | ID: covidwho-1309699

ABSTRACT

The time-varying reproduction number (Rt: the average number of secondary infections caused by each infected person) may be used to assess changes in transmission potential during an epidemic. While new infections are not usually observed directly, they can be estimated from data. However, data may be delayed and potentially biased. We investigated the sensitivity of Rt estimates to different data sources representing COVID-19 in England, and we explored how this sensitivity could track epidemic dynamics in population sub-groups. We sourced public data on test-positive cases, hospital admissions and deaths with confirmed COVID-19 in seven regions of England over March through August 2020. We estimated Rt using a model that mapped unobserved infections to each data source. We then compared differences in Rt with the demographic and social context of surveillance data over time. Our estimates of transmission potential varied for each data source, with the relative inconsistency of estimates varying across regions and over time. Rt estimates based on hospital admissions and deaths were more spatio-temporally synchronous than when compared to estimates from all test positives. We found these differences may be linked to biased representations of subpopulations in each data source. These included spatially clustered testing, and where outbreaks in hospitals, care homes, and young age groups reflected the link between age and severity of the disease. We highlight that policy makers could better target interventions by considering the source populations of Rt estimates. Further work should clarify the best way to combine and interpret Rt estimates from different data sources based on the desired use. This article is part of the theme issue 'Modelling that shaped the early COVID-19 pandemic response in the UK'.


Subject(s)
COVID-19/epidemiology , Pandemics , Bias , COVID-19/prevention & control , COVID-19/transmission , COVID-19/virology , England/epidemiology , Humans , SARS-CoV-2
5.
Philos Trans R Soc Lond B Biol Sci ; 376(1829): 20200266, 2021 07 19.
Article in English | MEDLINE | ID: covidwho-1309686

ABSTRACT

As several countries gradually release social distancing measures, rapid detection of new localized COVID-19 hotspots and subsequent intervention will be key to avoiding large-scale resurgence of transmission. We introduce ASMODEE (automatic selection of models and outlier detection for epidemics), a new tool for detecting sudden changes in COVID-19 incidence. Our approach relies on automatically selecting the best (fitting or predicting) model from a range of user-defined time series models, excluding the most recent data points, to characterize the main trend in an incidence. We then derive prediction intervals and classify data points outside this interval as outliers, which provides an objective criterion for identifying departures from previous trends. We also provide a method for selecting the optimal breakpoints, used to define how many recent data points are to be excluded from the trend fitting procedure. The analysis of simulated COVID-19 outbreaks suggests ASMODEE compares favourably with a state-of-art outbreak-detection algorithm while being simpler and more flexible. As such, our method could be of wider use for infectious disease surveillance. We illustrate ASMODEE using publicly available data of National Health Service (NHS) Pathways reporting potential COVID-19 cases in England at a fine spatial scale, showing that the method would have enabled the early detection of the flare-ups in Leicester and Blackburn with Darwen, two to three weeks before their respective lockdown. ASMODEE is implemented in the free R package trendbreaker. This article is part of the theme issue 'Modelling that shaped the early COVID-19 pandemic response in the UK'.


Subject(s)
COVID-19/epidemiology , Models, Theoretical , Pandemics , SARS-CoV-2/pathogenicity , Algorithms , COVID-19/transmission , COVID-19/virology , Communicable Disease Control , England/epidemiology , Humans , United Kingdom/epidemiology
6.
Nat Commun ; 12(1): 1942, 2021 03 29.
Article in English | MEDLINE | ID: covidwho-1157906

ABSTRACT

In early 2020 many countries closed schools to mitigate the spread of SARS-CoV-2. Since then, governments have sought to relax the closures, engendering a need to understand associated risks. Using address records, we construct a network of schools in England connected through pupils who share households. We evaluate the risk of transmission between schools under different reopening scenarios. We show that whilst reopening select year-groups causes low risk of large-scale transmission, reopening secondary schools could result in outbreaks affecting up to 2.5 million households if unmitigated, highlighting the importance of careful monitoring and within-school infection control to avoid further school closures or other restrictions.


Subject(s)
COVID-19/transmission , Family Characteristics , Schools/organization & administration , Adolescent , COVID-19/epidemiology , COVID-19/virology , Child , Child, Preschool , Disease Transmission, Infectious/prevention & control , England/epidemiology , Humans , Pandemics , Risk Assessment , Risk Factors , SARS-CoV-2/isolation & purification , Schools/statistics & numerical data
7.
PLoS Comput Biol ; 16(12): e1008409, 2020 12.
Article in English | MEDLINE | ID: covidwho-966830

ABSTRACT

Estimation of the effective reproductive number Rt is important for detecting changes in disease transmission over time. During the Coronavirus Disease 2019 (COVID-19) pandemic, policy makers and public health officials are using Rt to assess the effectiveness of interventions and to inform policy. However, estimation of Rt from available data presents several challenges, with critical implications for the interpretation of the course of the pandemic. The purpose of this document is to summarize these challenges, illustrate them with examples from synthetic data, and, where possible, make recommendations. For near real-time estimation of Rt, we recommend the approach of Cori and colleagues, which uses data from before time t and empirical estimates of the distribution of time between infections. Methods that require data from after time t, such as Wallinga and Teunis, are conceptually and methodologically less suited for near real-time estimation, but may be appropriate for retrospective analyses of how individuals infected at different time points contributed to the spread. We advise caution when using methods derived from the approach of Bettencourt and Ribeiro, as the resulting Rt estimates may be biased if the underlying structural assumptions are not met. Two key challenges common to all approaches are accurate specification of the generation interval and reconstruction of the time series of new infections from observations occurring long after the moment of transmission. Naive approaches for dealing with observation delays, such as subtracting delays sampled from a distribution, can introduce bias. We provide suggestions for how to mitigate this and other technical challenges and highlight open problems in Rt estimation.


Subject(s)
Basic Reproduction Number , COVID-19 , COVID-19/epidemiology , COVID-19/transmission , Computational Biology , Humans , Models, Statistical , SARS-CoV-2
8.
ProQuest Central; 2020.
Preprint in English | ProQuest Central | ID: ppcovidwho-2077

ABSTRACT

Background: Interventions are now in place worldwide to reduce transmission of the novel coronavirus. Assessing temporal variations in transmission in different countries is essential for evaluating the effectiveness of public health interventions and the impact of changes in policy. Methods: We use case notification data to generate daily estimates of the time-dependent reproduction number in different regions and countries. Our modelling framework, based on open source tooling, accounts for reporting delays, so that temporal variations in reproduction number estimates can be compared directly with the times at which interventions are implemented. Results: We provide three example uses of our framework. First, we demonstrate how the toolset displays temporal changes in the reproduction number. Second, we show how the framework can be used to reconstruct case counts by date of infection from case counts by date of notification, as well as to estimate the reproduction number. Third, we show how maps can be generated to clearly show if case numbers are likely to decrease or increase in different regions. Results are shown for regions and countries worldwide on our website (https://epiforecasts.io/covid/) and are updated daily. Our tooling is provided as an open-source R package to allow replication by others. Conclusions: This decision-support tool can be used to assess changes in virus transmission in different regions and countries worldwide. This allows policymakers to assess the effectiveness of current interventions, and will be useful for inferring whether or not transmission will increase when interventions are lifted. As well as providing daily updates on our website, we also provide adaptable computing code so that our approach can be used directly by researchers and policymakers on confidential datasets. We hope that our tool will be used to support decisions in countries worldwide throughout the ongoing COVID-19 pandemic.

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