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1.
Embase;
Preprint in English | EMBASE | ID: ppcovidwho-326899

ABSTRACT

A new SARS-CoV-2 variant of concern, Omicron (B.1.1.529), has been identified based on genomic sequencing and epidemiological data in South Africa. Presumptive Omicron cases in South Africa have grown extremely rapidly, despite high prior exposure and moderate vaccination coverage. The available evidence suggests that Omicron spread is at least in part due to evasion of this immune protection, though Omicron may also exhibit higher intrinsic transmissibility. Using detailed laboratory and epidemiological data from South Africa, we estimate the constraints on these two characteristics of the new variant and their relationship. Our estimates and associated uncertainties provide essential information to inform projection and scenario modeling analyses, which are crucial planning tools for governments around the world.

2.
PUBMED; 2021.
Preprint in English | PUBMED | ID: ppcovidwho-293295

ABSTRACT

Governments around the world have implemented non-pharmaceutical interventions (NPIs), e.g. physical distancing and travel restrictions, to limit the transmission of COVID-19. While lockdowns and physical distancing have proven effective for reducing COVID-19 transmission, there is still limited understanding of the degree to which these interventions impact disease transmission, and how they are reflected in measures of human behaviour. Further, there is a lack of understanding about how new sources of data can be used to monitor NPIs, where these data have the potential to augment existing disease surveillance and modelling efforts. In this study, we assess the relationship between indicators of human mobility, NPIs, and estimates of R t , a real-time measure of the intensity of COVID-19 transmission in subnational districts of Ghana using a multilevel generalised linear mixed model. We demonstrate a relationship between reductions in human mobility and decreases in R t during the early stages of the COVID-19 epidemic in Ghana, and show how reductions in human mobility relate to increasing stringency of NPIs. We demonstrate the utility of combining local disease surveillance data with large scale human mobility data to augment existing surveillance capacity to estimate and monitor the effect of NPI policies. Summary Box: What is already known?: NPI measures including physical distancing, reduction of travel, and use of personal protective equipment have been demonstrated to reduce COVID-19 transmission. Much of the existing research focuses on comparisons of NPI stringency with COVID-19 transmission among different countries, or on high-income countries. What are the new findings?: We show how human mobility and NPI stringency were associated with changes in R t using detailed COVID-19 surveillance and human mobility data from districts in Ghana. We further demonstrate how this association was strongest in the early COVID-19 outbreak in Ghana, decreasing after the relaxation of national restrictions. What do the new findings imply?: The change in association between human mobility, NPI stringency, and R t may reflect a "decoupling" of NPI stringency and human mobility from disease transmission in Ghana as the COVID-19 epidemic progressed. This has implications for public responses to the early stages of epidemic outbreaks and our understanding of the utility of mobility data for predicting the spread of COVID-19.

3.
PUBMED; 2021.
Preprint in English | PUBMED | ID: ppcovidwho-292934

ABSTRACT

Governments around the world have implemented non-pharmaceutical interventions (NPIs), e.g. physical distancing and travel restrictions, to limit the transmission of COVID-19. While lockdowns and physical distancing have proven effective for reducing COVID-19 transmission, there is still limited understanding of the degree to which these interventions impact disease transmission, and how they are reflected in measures of human behaviour. Further, there is a lack of understanding about how new sources of data can be used to monitor NPIs, where these data have the potential to augment existing disease surveillance and modelling efforts. In this study, we assess the relationship between indicators of human mobility, NPIs, and estimates of R t , a real-time measure of the intensity of COVID-19 transmission in subnational districts of Ghana using a multilevel generalised linear mixed model. We demonstrate a relationship between reductions in human mobility and decreases in R t during the early stages of the COVID-19 epidemic in Ghana, and show how reductions in human mobility relate to increasing stringency of NPIs. We demonstrate the utility of combining local disease surveillance data with large scale human mobility data to augment existing surveillance capacity to estimate and monitor the effect of NPI policies. Summary Box: What is already known?: NPI measures including physical distancing, reduction of travel, and use of personal protective equipment have been demonstrated to reduce COVID-19 transmission. Much of the existing research focuses on comparisons of NPI stringency with COVID-19 transmission among different countries, or on high-income countries. What are the new findings?: We show how human mobility and NPI stringency were associated with changes in R t using detailed COVID-19 surveillance and human mobility data from districts in Ghana. We further demonstrate how this association was strongest in the early COVID-19 outbreak in Ghana, decreasing after the relaxation of national restrictions. What do the new findings imply?: The change in association between human mobility, NPI stringency, and R t may reflect a "decoupling" of NPI stringency and human mobility from disease transmission in Ghana as the COVID-19 epidemic progressed. This has implications for public responses to the early stages of epidemic outbreaks and our understanding of the utility of mobility data for predicting the spread of COVID-19.

4.
Nat Commun ; 12(1): 5173, 2021 08 27.
Article in English | MEDLINE | ID: covidwho-1376196

ABSTRACT

Disease modelling has had considerable policy impact during the ongoing COVID-19 pandemic, and it is increasingly acknowledged that combining multiple models can improve the reliability of outputs. Here we report insights from ten weeks of collaborative short-term forecasting of COVID-19 in Germany and Poland (12 October-19 December 2020). The study period covers the onset of the second wave in both countries, with tightening non-pharmaceutical interventions (NPIs) and subsequently a decay (Poland) or plateau and renewed increase (Germany) in reported cases. Thirteen independent teams provided probabilistic real-time forecasts of COVID-19 cases and deaths. These were reported for lead times of one to four weeks, with evaluation focused on one- and two-week horizons, which are less affected by changing NPIs. Heterogeneity between forecasts was considerable both in terms of point predictions and forecast spread. Ensemble forecasts showed good relative performance, in particular in terms of coverage, but did not clearly dominate single-model predictions. The study was preregistered and will be followed up in future phases of the pandemic.


Subject(s)
COVID-19/epidemiology , COVID-19/virology , Forecasting , Germany/epidemiology , Humans , Models, Statistical , Pandemics/statistics & numerical data , Poland/epidemiology , SARS-CoV-2/physiology , Seasons
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