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Food environments can profoundly impact diet and related diseases. Effective, robust measures of food environment nutritional quality are required by researchers and policymakers investigating their effects on individual dietary behavior and designing targeted public health interventions. The most commonly used indicators of food environment nutritional quality are limited to measuring the binary presence or absence of entire categories of food outlet type, such as ‘fast-food’ outlets, which can range from burger joints to salad chains. This work introduces a summarizing indicator of restaurant nutritional quality that exists along a continuum, and which can be applied at scale to make distinctions between diverse restaurants within and across categories of food outlets. Verified nutrient data for a set of over 500 chain restaurants is used as ground-truth data to validate the approach. We illustrate the use of the validated indicator to characterize food environments at the scale of an entire jurisdiction, demonstrating how making distinctions between different shades of nutritiousness can help to uncover hidden patterns of disparities in access to high nutritional quality food.CCS CONCEPTSApplied computing → Life and medical sciences.

BackgroundThe burden of many infectious diseases varies seasonally and a better understanding of the drivers of infectious disease seasonality would help to improve public health interventions. For directly transmitted highly-immunizing childhood infections, the leading hypothesis is that seasonality is strongly driven by social gatherings imposed by schools, with maxima and minima during school terms and holidays respectively. However, we currently have a poor understanding of the seasonality of childhood infections in societies without schools and whether these are driven by human social gatherings. Here, we used unique nationwide data consisting of >40 epidemics over 100 years in 18th and 19th century Finland, an agricultural pre-health care society without schools, to (i) quantify the seasonality of three easily identifiable childhood infections, smallpox, pertussis and measles and (ii) test the extent to which seasonality of these diseases is driven by seasonal social gatherings.MethodsWe quantified the seasonality of transmission using time series Susceptible-Infected-Recovery models, wavelet analyses and general additive mixed models.ResultsWe found that all three infections were seasonal and the seasonality patterns differed from those in industrialized societies with schools. Smallpox and measles showed high transmission in the first half of the year, but we could not associate this with seasonal human gatherings events. For pertussis, however, transmission was higher during social gathering events such as New Year and Easter.ConclusionsOur results show that the seasonality of childhood infections is more variable than previously described in other populations and indicate a pathogen-specific role of human social aggregation in driving the infectious disease dynamics.FundingAcademy of Finland (278751, 292368), Nordforsk (104910), the Ehrnrooth Foundation, the Finnish Cultural Foundation, the University of Turku Foundation and the Doctoral Programme in Biology, Geography and Geology, University of Turku.Clinical trial numberNA

ObjectiveTo investigate differences of COVID-19 related mortality among women and men across sub-Saharan Africa (SSA) from the beginning of the pandemic.DesignA cross sectional study.SettingData from 20 member nations of the WHO African region until September 1, 2020.Participants69,580 cases of COVID-19, stratified by sex (men, n=43071; women, n=26509) and age (0-39 years, n=41682; 40-59 years, n=20757; 60+ years, n=7141).Main outcome measuresWe computed the SSA- and country-specific case fatality rates (CFRs) and sex-specific CFR differences across various age groups, using a Bayesian approach.ResultsA total of 1,656 (2.4% of total cases reported; 1656/69580) deaths were reported, with men accounting for 1168/1656 (70.5%) of total deaths. In SSA, women had a lower CFR than men (mean CFRdiff = -0.9%; 95% credible intervals -1.1% to -0.6%). The mean CFR estimates increased with age, with the sex-specific CFR differences being significant among those aged 40 or more (40-59 age-group: mean CFRdiff = -0.7%; 95% credible intervals -1.1% to -0.2%; 60+ age-group: mean CFRdiff = -3.9%; 95% credible intervals -5.3% to -2.4%). At the country level, seven of the twenty SSA countries reported significantly lower CFRs among women than men overall. Moreover, corresponding to the age-specific datasets, significantly lower CFRs in women than men were observed in the 60+ age-group in seven countries and 40-59 age-group in one country.Conclusions>Sex and age are important predictors of COVID-19 mortality. Countries should prioritize the collection and use of sex-disaggregated data to understand the evolution of the pandemic. This is essential to design public health interventions and ensure that policies promote a gender sensitive public health response.Summary BoxWhat is already known on this topicLittle is known on the impact of COVID-19 among different sexes and age-groups in sub-Saharan Africa (SSA).The availability of data on COVID-19 cases and deaths, disaggregated by both age and sex from the WHO African region has been scarce.In most of the non-African countries, sex-specific COVID-19 severity and mortality were substantially worse for men than for women, during the first wave of the novel coronavirus (COVID-19) pandemic.What this study addsTo the best of our knowledge, this is the largest study focussing on the COVID-19 related fatalities among men and women in SSA, and it confirmed that both sex and age are important predictors of COVID-19 mortality in SSA, similar to other regions.In SSA, overall, men had a higher case fatality rate (CFR) than women. When disaggregated by age, this difference persisted only in individuals aged 40 or more. 7 among the 20 SSA countries included in this study also reported significantly higher CFRs in men than women for the age-aggregated dataset.Public health prevention activities and responses should take into account gender differences in terms of disease severity and mortality, especially among men aged 40 or more in SSA.

BackgroundSince COVID-19 erupted in the United States, little is known about how state-level opioid overdose trends and decedent characteristics have varied throughout the country.ObjectiveInvestigate changes in annual overdose death rates, substances involved, and decedent demographics in opioid overdose deaths across nine states; assess whether 2019-2020 trends were emerging (i.e., change from 2019-2020 was non-existent from 2018-2019) or continuing (i.e., change from 2019-2020 existed from 2018-2019).DesignCross-sectional study using vital statistics data to conduct a retrospective analysis comparing 2020 to 2019 and 2019 to 2018 across nine states.SettingAlaska, Colorado, Connecticut, Indiana, Massachusetts, North Carolina, Rhode Island, Utah, and Wyoming.ParticipantsOpioid-related overdose deaths in 2018, 2019, and 2020.MeasurementsAnnual overdose death rate, proportion of overdose deaths involving specific substances, and decedent demographics (age, sex, race, and ethnicity).ResultsWe find emerging increases in annual opioid-related overdose death rates in Alaska (55.3% [P=0.020]), Colorado (80.2% [P<0.001]), Indiana (40.1% [P=0.038]), North Carolina (30.5% [P<0.001]), and Rhode Island (29.6% [P=0.011]). Decreased heroin-involved overdose deaths were emerging in Alaska (−49.5% [P=0.001]) and Indiana (−58.8% [P<0.001]), and continuing in Colorado (−33.3% [P<0.001]), Connecticut (−48.2% [P<0.001]), Massachusetts (39.9% [P<0.001]), and North Carolina (−34.8% [P<0.001]). Increases in synthetic opioid presence were emerging in Alaska (136.5% [P=0.019]) and Indiana (27.6% [P<0.001]), and continuing in Colorado (44.4% [P<0.001]), Connecticut (3.6% [P<0.05]), and North Carolina (14.6% [P<0.001]). We find emerging increases in the proportion of male decedents in Colorado (15.2% [P=0.008]) and Indiana (12.0% [P=0.013]).LimitationsDelays from state-specific death certification processes resulted in varying analysis periods across states.ConclusionThese findings highlight emerging changes in opioid overdose dynamics across different states, which can inform state-specific public health interventions.

SummaryTo prevent the catastrophic health and economic consequences from COVID-19 epidemics, some nations have aimed for no community transmission outside of quarantine. To achieve this, governments have had to respond rapidly to outbreaks with public health interventions. But the exact characteristics of an outbreak that trigger these measures differ and are poorly defined. We used existing data from epidemics in Australia to establish a practical model to assist stakeholders in making decisions about the optimal timing and extent of interventions. We found that the number of reported cases on the day that interventions commenced strongly predicted the size of the outbreaks. We quantified how effective interventions were at containing outbreaks in relation to the number of cases at the time the interventions commenced. We also found that containing epidemics from novel variants that had higher transmissibility would require more stringent interventions that commenced earlier. In contrast, increasing vaccination coverage would enable more relaxed interventions. Our model highlights the importance of early and decisive action in the early phase of an outbreak if governments aimed for zero community transmission, although new variants and vaccination coverage may change this.

India has seen a surge of SARS-CoV-2 infections and deaths in early part of 2021, despite having controlled the epidemic during 2020. Building on a two-strain, semi-mechanistic model that synthesizes mortality and genomic data, we find evidence that altered epidemiological properties of B.1.617.2 (Delta) variant play an important role in this resurgence in India. Under all scenarios of immune evasion, we find an increased transmissibility advantage for B.1617.2 against all previously circulating strains. Using an extended SIR model accounting for reinfections and wanning immunity, we produce evidence in support of how early public interventions in March 2021 would have helped to control transmission in the country. We argue that enhanced genomic surveillance along with constant assessment of risk associated with increased transmission is critical for pandemic responsiveness.One Sentence SummaryAltered epidemiological characteristics of B.1.617.2 and delayed public health interventions contributed to the resurgence of SARS-CoV-2 in India from February to May 2021.

The COVID-19 pandemic has highlighted the importance of non-pharmacological interventions (NPIs) for controlling epidemics of emerging infectious diseases. Despite their importance, NPIs have been monitored mainly through the manual efforts of volunteers. This approach hinders measurement of the NPI effectiveness and development of evidence to guide their use to control the global pandemic. We present EpiTopics, a machine learning approach to support automation of NPI prediction and monitoring at both the document level and country level by mining the vast amount of unlabeled news reports on COVID-19. EpiTopics uses a 3-stage, transfer-learning algorithm to classify documents according to NPI categories, relying on topic modeling to support result interpretation. We identified 25 interpretable topics under 4 distinct and coherent COVID-related themes. Importantly, the use of these topics resulted in significant improvements over alternative automated methods in predicting the NPIs in labeled documents and in predicting country-level NPIs for 42 countries., The bigger picture Accurate, scalable detection of the timing of changes to public health interventions for COVID-19 is an important step toward automating evaluation of the effectiveness of interventions. We show that it is possible to train an interpretable deep-learning model called EpiTopics on media news data to predict (1) the interventions mentioned in individual news articles and (2) the temporal change of intervention status at the country level. We addressed a main challenge of label scarcity among the news reports. Using EpiTopics, we modeled the latent semantics from 1.2 million unlabeled news reports on COVID-19 over 42 countries recorded from November 1, 2019 to July 31, 2020, identifying 25 interpretable topics under 4 COVID-related themes. Using the learned topic model, we inferred topic mixture membership for each labeled article, which allowed us to learn an accurate connection between the topics and the public health interventions at both the document level and country level., We developed a machine learning model called EpiTopics toward automatic detection of the changes in the status of non-pharmacological interventions (NPI) for COVID-19 from news reports. EpiTopics learns country-dependent topics from large numbers of COVID-19 news reports that do not have NPI labels. Subsequently, EpiTopics learns accurate connections between these topics and changes in NPI status from a set of labeled news reports, which enables accurate detection of temporal NPI status for each country referred to in the news reports.

Public health interventions to decrease the spread of SARS-CoV-2 were largely implemented in the United States during spring 2020. This study evaluates the additional effects of these interventions on non-SARS-CoV-2 respiratory viral infections from a single healthcare system in the San Francisco Bay Area. The results of a respiratory pathogen multiplex polymerase chain reaction panel intended for inpatient admissions were analyzed by month between 2019 and 2020. We found major decreases in the proportion and diversity of non-SARS-CoV-2 respiratory viral illnesses in all months following masking and shelter-in-place ordinances. These findings suggest real-world effectiveness of nonpharmaceutical interventions on droplet-transmitted respiratory infections.

Recent studies have identified notable disparities in SARS-CoV-2 infection risk among ethnic minorities. We evaluated SARS-CoV-2 test results from individuals presenting for testing in Los Angeles between June-December, 2020. We calculated prevalence ratios for various employment categories. Among 518,914 test results, of which 295,295 (56.9%) were from individuals reporting Hispanic ethnicity, SARS-CoV-2 positivity was 16.5% among Hispanic individuals compared to 5.0% among non-Hispanic individuals (p-value

Tracking the emergence and spread of SARS-CoV-2 is critical to inform public health interventions. Phylodynamic analyses have quantified SARS-CoV-2 migration on global and local scales1–5, yet they have not been applied to determine transmission dynamics in Canada. We quantified SARS-CoV-2 migration into, within, and out of Canada in the context of COVID-19 travel restrictions. To minimize sampling bias, global sequences were subsampled with probabilities corrected for their countries’ monthly contribution to global new diagnoses. A time-scaled maximum likelihood tree was used to estimate most likely ancestral geographic locations (country or Canadian province), enabling identification of sublineages, defined as introduction events into Canada resulting in domestic transmission. Of 402 Canadian sublineages identified, the majority likely originated from the USA (54%), followed by Russia (7%), India (6%), Italy (6%), and the UK (5%). International introductions were mostly into Ontario (39%) and Quebec (38%). Among Pango lineages6, B.1 was imported at least 191 separate times from 11 different countries. Introduction rates peaked in late March then diminished but were not eliminated following national interventions including restrictions on non-essential travel. We further identified 1,380 singleton importations, international importations that did not result in further sampled transmission, whereby representation of lineages and location were comparable to sublineages. Although proportion of international transmission decreased over time, this coincided with exponential growth of within-province transmission – in fact, total number of sampled transmission events from international or interprovincial sources increased from winter 2020 into spring 2020 in many provinces. Ontario, Quebec, and British Columbia acted as sources of transmission more than recipients, within the caveat of higher sequence representation. We present strong evidence that international introductions and interprovincial transmission of SARS-CoV-2 contributed to the Canadian COVID-19 burden throughout 2020, despite initial reductions mediated by travel restrictions in 2020. More stringent border controls and quarantine measures may have curtailed introductions of SARS-CoV-2 into Canada and may still be warranted.Significance StatementBy analyzing SARS-CoV-2 genomes from Canada in the context of the global pandemic, we illuminate the extent to which the COVID-19 burden in Canada was perpetuated by ongoing international importations and interprovincial transmission throughout 2020. Although travel restrictions enacted in March 2020 reduced the importation rate and proportion of transmission from abroad across all Canadian provinces, SARS-CoV-2 introductions from the USA, India, Russia, and other nations were detectable through the summer and fall of 2020.

Previous work has shown that environment affects SARS-CoV-2 transmission, but it is unclear whether emerging strains show similar responses. Here we show that, like other SARS-CoV-2 strains, lineage B.1.1.7 spread with greater transmission in colder and more densely populated parts of England. However, we also find evidence of B.1.1.7 having a transmission advantage at warmer temperatures compared to other strains. This implies that spring and summer conditions are unlikely to slow B.1.1.7’s invasion in Europe and across the Northern hemisphere - an important consideration for public health interventions.

ContextContact tracing has been a central COVID-19 transmission control measure. However, without the consideration of the needs of specific populations, public health interventions can exacerbate health inequities.PurposeThe purpose of this rapid review was to determine if and how health inequities were included in the design of contact tracing interventions in epidemic settings.MethodWe conducted a search of the electronic databases MEDLINE and Web of Science. Our inclusion criteria included articles that: (i) described the design of contact tracing interventions, (ii) have been published between 2013 and 2020 in English, French, Spanish, Chinese, or Portuguese, (iii) and included at least 50% of empiricism, according to the Automated Classifier of Texts on Scientific Studies (ATCER) tool. We relied on various tools to extract data.ResultFollowing the titles and abstracts screening of 230 articles, 39 articles met the inclusion criteria. Only seven references were retained after full text review. None of the selected studies considered health inequities in the design of contact tracing interventions.ConclusionThe use of tools/concepts for incorporating health inequities, such as the REFLEX-ISS tool, and “proportionate universalism” when designing contact tracing interventions, would enable practitioners, decision makers, and researchers to better consider health inequities.

In April 2020, we developed a COVID-19 transmission model used as part of RAND’s web-based COVID-19 decision support tool that compares the effects of different nonphar-maceutical public health interventions (NPIs) on health and economic outcomes. An interdis-ciplinary approach informed the selection and use of multiple NPIs, combining quantitative modeling of the health/economic impacts of interventions with qualitative assessments of other important considerations (e.g., cost, ease of implementation, equity). We previously published a description of our approach as a RAND report describing how the epidemiological model, the economic model, and a systematic assessment of NPIs informed the web-tool. This paper provides further details of our model, describes extensions that we made to our model since April, presents sensitivity analyses, and analyzes periodic NPIs. Our findings suggest that there are opportunities to shape the tradeoffs between economic and health outcomes by carefully evaluating a more comprehensive range of reopening policies. We consider strategies that periodically switch between a base NPI level and a higher NPI level as our working example.

We investigated SARS-CoV-2 transmission dynamics in Italy, one of the countries hit hardest by the pandemic, using phylodynamic analysis of viral genetic and epidemiological data. We observed the co-circulation of at least 13 different SARS-CoV-2 lineages over time, which were linked to multiple importations and characterized by large transmission clusters concomitant with a high number of infections. Subsequent implementation of a three-phase nationwide lockdown strategy greatly reduced infection numbers and hospitalizations. Yet we present evidence of sustained viral spread among sporadic clusters acting as “hidden reservoirs” during summer 2020. Mathematical modelling shows that increased mobility among residents eventually catalyzed the coalescence of such clusters, thus driving up the number of infections and initiating a new epidemic wave. Our results suggest that the efficacy of public health interventions is, ultimately, limited by the size and structure of epidemic reservoirs, which may warrant prioritization during vaccine deployment.

The recent publication of the Great Barrington Declaration (GBD), which calls for relaxing all public health interventions on young, healthy individuals, has brought the question of herd immunity to the forefront of COVID-19 policy discussions, and is partially based on unpublished research that suggests low herd immunity thresholds (HITs) of 10-20%. We re-evaluate these findings and correct a flawed assumption leading to COVID-19 HIT estimates of 60-80%. If policymakers were to adopt a herd immunity strategy, in which the virus is allowed to spread relatively unimpeded, we project that cumulative COVID-19 deaths would be five times higher than the initial estimates suggest. Our re-estimates of the COVID-19 HIT corroborate strong signals in the data and compelling arguments that most of the globe remains far from herd immunity, and suggest that abandoning community mitigation efforts would jeopardize the welfare of communities and integrity of healthcare systems.

BackgroundMany countries have attempted to mitigate and control COVID-19 through the implementation of non-pharmaceutical interventions, particularly with the aim of reducing population movement and contact. However, it remains unclear how the different control strategies impacted the local phylodynamics of the causative SARS-CoV-2 virus.AimTo assess the duration of chains of virus transmission within individual countries and the extent to which countries export viruses to their geographic neighbours.MethodsTo address core questions in genomic epidemiology and public health we analysed complete SARS-CoV-2 genomes to infer the relative frequencies of virus importation and exportation, as well as virus transmission dynamics, within countries of northern Europe. To this end, we examined virus evolution and phylodynamics in Denmark, Finland, Iceland, Norway and Sweden during the first year of the pandemic.ResultsThe Nordic countries differed markedly in the invasiveness of control strategies implemented. In particular, Sweden did not initially employ any strict population movement limitations and experienced markedly different transmission chain dynamics, which were more numerous and tended to have more cases, a set of features that increased with time during the first eight months of 2020.ConclusionTogether with Denmark, Sweden was also characterised as a net exporter of SARS-CoV-2. Hence, Sweden effectively constituted an epidemiological and evolutionary ‘refugia’ that enabled the virus to maintain active transmission and spread to other geographic localities. In sum, our analysis reveals the utility of genomic surveillance where active transmission chain monitoring is a key metric.

Wastewater monitoring for SARS-CoV-2 has been suggested as an epidemiological indicator of community infection dynamics and disease prevalence. We report wastewater viral RNA levels of SARS-CoV-2 in a major metropolis serving over 3.6 million people geographically spread over 39 distinct sampling sites. Viral RNA levels were followed weekly for 22 weeks, both before, during, and after a major surge in cases, and simultaneously by two independent laboratories. We found SARS-CoV-2 RNA wastewater levels were a strong predictive indicator of trends in the nasal positivity rate two-weeks in advance. Furthermore, wastewater viral RNA loads demonstrated robust tracking of positivity rate for populations served by individual treatment plants, findings which were used in real-time to make public health interventions, including deployment of testing and education strike teams.

Non-pharmaceutical public health interventions have significant economic and social costs, and minimizing their duration is paramont. Assuming that interventions are sufficient to reduce infection prevalence, we use a simple linear SIR model with case importation to determine the relationship between the timing of restrictions, duration of measures necessary to return the incidence to a set point, and the final size of the outbreak. The predictions of our linear SIR model agree well with COVID-19 data from Atlantic Canada, and are consistent with the predictions of more complex deterministic COVID-19 models. We conclude that earlier re-escalation of restrictions results in shorter disruptions, smaller outbreaks, and consequently, lower economic and social costs. Our key message is succinctly summarized as ‘don’t wait, re-escalate’ since delaying re-escalation of restrictions results in not only more infections, but also longer periods of restrictions.

Helminthiases are a class of neglected tropical diseases that affect at least one billion people worldwide, with a disproportionate impact in resource-poor areas with limited disease surveillance. Geospatial methods can offer valuable insights into the burden of these infections, particularly given that many are subject to strong ecological influences on the environmental, vector-borne, or zoonotic stages of their life cycle. In this study, we screened 6,829 abstracts and analyzed 485 studies that use maps to document, infer, or predict transmission patterns for over 200 species of parasitic worm. We found that quantitative mapping methods are increasingly used in medical parasitology, drawing on One Health surveillance data from the community scale to model geographic distributions and burdens up to the regional or global scale. However, we found that the vast majority of the human helminthiases may be entirely unmapped, with research effort focused disproportionately on a half-dozen infections that are targeted by mass drug administration programs. Entire regions were also surprisingly under-represented in the literature, particularly southern Asia and the Neotropics. We conclude by proposing a shortlist of possible priorities for future research, including several neglected helminthiases with a burden that may be substantially underestimated.

As of August 27, 2020, the number of cumulative cases of COVID-19 in the US exceeded 5,863,363 and included 180,595 deaths, thus causing a serious public health crisis. Curbing the spread of Covid-19 is still urgently needed. Given the lack of potential vaccines and effective medications, non-pharmaceutical interventions are the major option to curtail the spread of COVID-19. An accurate estimate of the potential impact of different non-pharmaceutical measures on containing, and identify risk factors influencing the spread of COVID-19 is crucial for planning the most effective interventions to curb the spread of COVID-19 and to reduce the deaths. Additive model-based bivariate causal discovery for scalar factors and multivariate Granger causality tests for time series factors are applied to the surveillance data of lab-confirmed Covid-19 cases in the US, University of Maryland Data (UMD) data, and Google mobility data from March 5, 2020 to August 25, 2020 in order to evaluate the contributions of social-biological factors, economics, the Google mobility indexes, and the rate of the virus test to the number of the new cases and number of deaths from COVID-19. We found that active cases/1000 people, workplaces, tests done/1000 people, imported COVID-19 cases, unemployment rate and unemployment claims/1000 people, mobility trends for places of residence (residential), retail and test capacity were the most significant risk factor for the new cases of COVID-19 in 23, 7, 6, 5, 4, 2, 1 and 1 states, respectively, and that active cases/1000 people, workplaces, residential, unemployment rate, imported COVID cases, unemployment claims/1000 people, transit stations, mobility trends (transit), tests done/1000 people, grocery, testing capacity, retail, percentage of change in consumption, percentage of working from home were the most significant risk factor for the deaths of COVID-19 in 17, 10, 4, 4, 3, 2, 2, 2, 1, 1, 1, 1 states, respectively. We observed that no metrics showed significant evidence in mitigating the COVID-19 epidemic in FL and only a few metrics showed evidence in reducing the number of new cases of COVID-19 in AZ, NY and TX. Our results showed that the majority of non-pharmaceutical interventions had a large effect on slowing the transmission and reducing deaths, and that health interventions were still needed to contain COVID-19.

We estimate the basic reproductive number and case counts for 15 distinct SARS-CoV-2 outbreaks, distributed across 10 countries and one cruise ship, based solely on phylodynamic analyses of genomic data. Our results indicate that, prior to significant public health interventions, the reproductive numbers for a majority (10) of these outbreaks are similar, with median posterior estimates ranging between 1.4 and 2.8. These estimates provide a view which is complementary to that provided by those based on traditional line listing data. The genomic-based view is arguably less susceptible to biases resulting from differences in testing protocols, testing intensity, and import of cases into the community of interest. In the analyses reported here, the genomic data primarily provides information regarding which samples belong to a particular outbreak. We observe that once these outbreaks are identified, the sampling dates carry the majority of the information regarding the reproductive number. Finally, we provide genome-based estimates of the cumulative case counts for each outbreak, which allow us to speculate on the amount of unreported infections within the populations housing each outbreak. These results indicate that for the majority (7) of the populations studied, the number of recorded cases is much bigger than the estimated cumulative case counts, suggesting the presence of unsequenced pathogen diversity in these populations.Significance StatementSince the beginning of the COVID-19 outbreak in late 2019, researchers around the globe have sought to estimate the rate at which the disease spread through populations prior to public health intervention, as quantified by the parameter R0. This is often estimated based on case count data and may be biased due to the presence of import cases. To overcome this, we estimate R0 by applying Bayesian phylodynamic methods to SARS-CoV-2 genomes which have been made available by laboratories worldwide. We provide R0 and absolute infection count estimates for 15 distinct outbreaks. These estimates contribute to our understanding of the baseline transmission dynamics of the disease, which will be critical in guiding future public health responses to the pandemic.

New York City experienced a large COVID-19 pandemic wave during March – May 2020. We model the transmission dynamics of COVID-19 in the city during the pandemic and estimate the effectiveness of public health interventions (overall and for each major intervention separately) for the entire population and by age group. We estimate that the overall effective reproductive number was 2.99 at the beginning of the pandemic wave and reduced to 0.93 one week after the stay-at-home mandate. Most age groups experienced similar reductions in transmission. Interventions reducing contact rates were associated with a 70.7% (95% CI: 65.0 – 76.4%) reduction of transmission overall and > 50% for all age groups during the pandemic. Face covering was associated with a 6.6% (95% CI: 0.8 – 12.4%) reduction of transmission overall and up to 20% for 65+ year-olds during the first month of implementation. Accounting for the amount of time masks are in use (i.e. mainly outside homes), these findings indicate universal masking could reduce transmission by up to 28-32% when lockdown-like measures are lifted, if the high effectiveness estimated for older adults were achieved for all ages. These estimates are verified by out-of-fit projections and support the need for implementing multiple interventions simultaneously in order to effectively mitigate the spread of COVID-19.

COVID-19 CG (covidcg.org) is an open resource for tracking SARS-CoV-2 single-nucleotide variations (SNVs), lineages, and clades using the virus genomes on the GISAID database while filtering by location, date, gene, and mutation of interest. COVID-19 CG provides significant time, labor, and cost-saving utility to projects on SARS-CoV-2 transmission, evolution, diagnostics, therapeutics, vaccines, and intervention tracking. Here, we describe case studies in which users can interrogate (1) SNVs in the SARS-CoV-2 spike receptor binding domain (RBD) across different geographical regions to inform the design and testing of therapeutics, (2) SNVs that may impact the sensitivity of commonly used diagnostic primers, and (3) the emergence of a dominant lineage harboring an S477N RBD mutation in Australia in 2020. To accelerate COVID-19 efforts, COVID-19 CG will be upgraded with new features for users to rapidly pinpoint mutations as the virus evolves throughout the pandemic and in response to therapeutic and public health interventions.The discovery of faster spreading variants of the virus that causes coronavirus disease 2019 (COVID-19) has raised alarm. These new variants are the result of changes (called mutations) in the virus’ genetic code. Random mutations can occur each time a virus multiplies. Although most mutations do not introduce any meaningful changes, some can alter the characteristics of the virus, for instance, helping the virus to spread more easily, reinfecting people who have had COVID-19 before, or reducing the sensitivity to treatments or vaccines. Scientists need to know about mutations in the virus that make treatments or vaccines less effective as soon as possible, so they can adjust their pandemic response. As a result, tracking these genetic changes is essential. But individual scientists or public health agencies may not have the staff, time or computer resources to extract usable information from the growing amount of genetic data available. A free online tool created by Chen et al. may help scientists and public health officials to track changes to the virus more easily. The COVID-19 CoV Genetics tool (COVID-19 CG) can quickly provide information on which virus mutations are present in an area during a specific period. It does this by processing data on mutations found in viral genetic material collected worldwide from hundreds of thousands of people with COVID-19, which are hosted in an existing online database. The COVID-19 CG tool presents customizable, interactive visualizations of the data. Thousands of scientists, public health agencies, and COVID-19 vaccine and treatment developers in over 100 countries are already using the COVID-19 CG tool to find the most common mutations in their area and use it for research. They can use this information to develop more effective vaccines or treatments. Chen et al. plan to update and improve the tool as more information becomes available to help advance global efforts to end the COVID-19 pandemic.

Background An accurate measure of the impact of COVID-19 is the infection fatality ratio, or the proportion of deaths among those infected, which is independent of variable testing rates between nations. The risk of mortality from COVID-19 depends strongly on age and current single estimates of the infection fatality ratio do not account for differences in national age profiles. In addition, it is unclear whether age influences cumulative death trajectories, or if differences between regions are because of the effect and timing of public health interventions. Our objective is to determine whether (1) infection fatality ratios and (2) death trajectories are clustered into more than one group due to differences in national age profiles. Methods National age standardised infection fatality ratios were derived from age stratified estimates from China and population estimates from the World Health Organisation. The infection fatality ratios were clustered into groups using Gaussian mixture models. Trajectory analysis clustered cumulative death rates at two time points, 50 and 150 days after the first reported death. Findings Infection fatality ratios from 201 nations were clustered into three groups: young, middle and older, with corresponding means (SD) of 0.20% (0.03%), 0.38% (0.11%) and 0.93% (0.21%). At 50 and 150 days, there were two and three clusters, respectively, of cumulative death trajectories from 122 nations with at least 25 deaths reported at 100 days. The first cluster had steadily increasing or stable cumulative death rates, while the second and third clusters had moderate and fast increases in rates, respectively. Fifty-eight nations changed cluster group membership between time points. There was an association between the infection fatality ratio clusters and the change in trajectory clusters between 50 and 150 days (p=0.014). Conclusion Differences in national age profiles created three clusters in the COVID-19 infection fatality ratio, with the impact on younger nations less than the current estimate 0.5-1.0%. National cumulative death rates were clustered into steady, moderate or fast trajectories. Changes in death rate trajectories between 50 and 150 days were associated with the infection fatality ratio clusters, however evidence for the influence of age on death trajectories is mixed.

SummaryHousehold contact studies are frequently used in tuberculosis transmission research, and models based on them often focus on transmission within the household. This contradicts recent research which suggests the transmission may be more likely to happen outside the household than within the household in high burden settings where these studies are frequently conducted. Consequently, most models would lead to biased estimates and misleading public health interventions. There is a strong need for developing models that allow concurrent estimation of household and extra-household transmission. In this study, we develop a random directed graph model for tuberculosis transmission, which permits users to concurrently build models for both household and extra-household transmission. Furthermore, our model can estimate the relative frequency of household transmission versus extra-household transmission and consistently produce unbiased estimates for risk factors, regardless of whether community controls are available. We illustrate our approach with a household contact study conducted in Vitória, Brazil, and our results indicate that extra-household transmission can account for 63% to 98% of M. tuberculosis infections detected during such a study.

Diaper need is a form of material hardship that acutely affects families with young children, is not currently addressed by US antipoverty programs, and has received little public or scientific attention. This study examined the association between diaper need and risk for food insecurity in a statewide sample of participants in the Special Supplemental Nutrition Program for Women, Infants, and Children (WIC). Families enrolled in Vermont WIC in August 2019 were invited to an online survey. Generalized linear models were used to estimate the relationship between risk for food insecurity measured by the Hunger Vital Sign tool and diaper need, with and without adjustment for household factors. Follow-up questions asked those with diaper need what they do when they run out of diapers and those without diaper need how they access enough diapers. Complete data were available for 501 households. Over half (52.3%) were at risk for food insecurity and nearly one-third (32.5%) reported diaper need. The odds of experiencing risk for food insecurity were 3.852 (95% CI=2.557, 5.803) times greater for families with diaper need than for families that had enough diapers. The association persisted with adjustment for location, age of respondent, number of children in diapers, and length of time participating in WIC (adjusted OR=4.036, 95% CI=2.645, 6.160). Strategies to avoid running out of diapers included borrowing, stretching supplies, switching to cloth or underwear, and buying on credit. It is possible that public health interventions that address diaper need may reduce food insecurity in households with children.

SummaryHuman mobility was associated with epidemic changes of coronavirus disease 2019 (COVID-19) in China, where strict public health interventions reduced human mobility and COVID-19 epidemics. But its association with COVID-19 epidemics in the European Union (EU) is unclear. In this quasi-experimental study, we modelled the temporal trends in human mobility and epidemics of COVID-19 in the 27 EU states between January 15 and May 9, 2020. COVID-19 and human mobility had 3 trend-segments, including an upward trend in COVID-19 daily incidence and a downward trend in most human mobilities in the middle segment. Compared with the EU states farther from Italy, the state-wide lockdown dates were more likely linked to turning points of human mobilities in the EU states closer to Italy, which were also more likely linked to second turning points of COVID-19 epidemics. Among the examined human mobilities, the second turning points in driving mobility and the first turning points in parks mobility were the best factors that connected lockdown dates and COVID-19 epidemics in the EU states closer to Italy. Our findings highlight the state- and mobility-heterogeneity in the associations of public health interventions and human mobility with changes of COVID-19 epidemics in the EU states.

SummaryBackgroundThe government of India implemented social distancing interventions to contain the COVID-19 epidemic. However, effects on epidemic dynamics are yet to be understood.MethodsRates of laboratory-confirmed COVID-19 infections per day and effective reproduction number (Rt) were estimated for 4 periods (Pre-lockdown and Lockdown Phases 1 to 3) according to nationally implemented phased interventions. Adoption of these interventions was estimated using Google mobility data. Estimates at the national level and for 12 Indian states most affected by COVID-19 are presented.FindingsDaily case rates ranged from 0·03 to 30·05/10 million people across 4 discrete periods in India. From May 4-17, 2020, the National Capital Territory (NCT) of Delhi had the highest case rate (222/10 million people/day), whereas Kerala had the lowest (2·18/10 million/day). AverageRtwas 1·99 (95% CI 1·93-2·06) for India; it ranged from 1·38 to 2·78, decreasing over time. Median mobility in India decreased in all contact domains, with the lowest being 21% in retail/recreation (95% CI 13-46%), except home which increased to 129% (95% CI 117-132%) compared to the 100% baseline value.InterpretationThe Indian government imposed strict contact mitigation, followed by a phased relaxation, which slowed the spread of COVID-19 epidemic progression in India. The identified daily COVID-19 case rates andRtwill aid national and state governments in formulating ongoing COVID-19 containment plans. Furthermore, these findings may inform COVID-19 public health policy in developing countries with similar settings to India.FundingNon-funded.

In this paper, we combine algorithm of Liu & West for the Particle Filter (PF) with SIRU-type epidemic model to monitor and forecast cases of Covid-19 in Brazil from February up to September. We filter the number of cumulative reported cases and estimate model parameters and more importantly unreported infectious cases (asymptomatic and symptomatic infectious individuals). The parameters under study are related to the attenuation factor of the transmission rate and the fraction of asymptomatic infectious becoming reported as symptomatic infectious. Initially, the problem is analysed through Particle Swarm Optimization (PSO) based simulations to provide initial guesses, which are then refined by means of PF simulations. Subsequently, two additional steps are performed to verify the capability of the adjusted model to predict and forecast new cases. According to the results, the pandemic peak is expected to take place in mid-June 2020 with about 25,000 news cases per day. As medical and hospital resources are limited, this result shows that public health interventions are essential and should not be relaxed prematurely, so that the coronavirus pandemic is controlled and conditions are available for the treatment of the most severe cases.

Background: The SARS-CoV-2 disease 2019 (COVID-19) pandemic has spread across the world with varying impact on health systems and outcomes. We assessed how the type and timing of public- health interventions impacted the course of the outbreak in Alberta and other Canadian provinces. Methods: We used publicly-available data to summarize rates of laboratory data and mortality in relation to measures implemented to contain the outbreak and testing strategy. We estimated the transmission potential of SARS-CoV-2 before the state of emergency declaration for each province (R0) and at the study end date (Rt). Results: The first cases were confirmed in Ontario (January 25) and British Columbia (January 28). All provinces implemented the same health-policy measures between March 12 and March 30. Alberta had a higher percentage of the population tested (3.8%) and a lower mortality rate (3/100,000) than Ontario (2.6%; 11/100,000) or Quebec (3.1%; 31/100,000). British Columbia tested fewer people (1.7%) and had similar mortality as Alberta. Data on provincial testing strategies were insufficient to inform further analyses. Mortality rates increased with increasing rates of lab- confirmed cases in Ontario and Quebec, but not in Alberta. R0 was similar across all provinces, but varied widely from 2.6 (95% confidence intervals 1.9-3.4) to 6.4 (4.3-8.5), depending on the assumed time interval between onset of symptoms in a primary and a secondary case (serial interval). The outbreak is currently under control in Alberta, British Columbia and Nova Scotia (Rt

Though ideal for determining the burden of disease, SARS-CoV2 test shortages preclude its implementation as a robust surveillance system in the US. We correlated the use of the derivative influenza-negative influenza-like illness (fnILI) z-score from the CDC as a proxy for incident cases and disease-specific deaths. For every unit increase of fnILI z-score, the number of cases increased by 70.2 (95%CI[5.1,135.3]) and number of deaths increased by 2.1 (95%CI[1.0,3.2]). FnILI data may serve as an accurate outcome measurement to track the spread of the and allow for informed and timely decision-making on public health interventions.

We introduce a novel mathematical model to analyze the effect of removing non-pharmaceutical interventions on the spread of COVID19 as a function of disease testing rate. We find that relaxing interventions has a strong impact on the size of the epidemic peak as a function of intervention removal time. We show that it is essential for predictive models to explicitly capture transmission from asymptomatic carriers and important to obtain precise information on asymptomatic transmission by testing. The asymptomatic reservoir, reported to account for as much as 85% of transmission, will contribute to resurgence of the epidemic if public health interventions are removed too soon. Use of more basic models that fail to capture asymptomatic transmission can result in large errors in predicted clinical caseload or in fitted epidemiological parameters and, therefore, may be unreliable in estimating the risk of a second wave based on the timing of terminated interventions.

As the COVID-19 pandemic spreads across the world, it is important to understand its features and responses to public health interventions in real-time. The field of infectious diseases epidemiology has highly advanced modeling strategies that yield relevant estimates. These include the doubling time of the epidemic and various other representations of the numbers of cases identified over time. Crude estimates of these quantities suffer from dependence on the underlying testing strategies within communities. We clarify the functional relationship between testing and the epidemic parameters, and thereby derive sensitivity analyses that explore the range of possible truths under various testing dynamics. We derive the required adjustment to the estimates of interest for New York City. We demonstrate that crude estimates that assume stable testing or complete testing can be biased.

Background: In Korea, a total of 10,840 confirmed cases of COVID-19 including 256 deaths have been recorded as of May 9, 2020. The time-delay adjusted case fatality risk (CFR) of COVID-19 in Korea is yet to be estimated. Methods: We obtained the daily series of confirmed cases and deaths in Korea reported prior to May 9, 2020. Using statistical methods, we estimated the time-delay adjusted risk for death from COVID-19 in Daegu, Gyeongsangbuk-do, other regions in Korea, as well as the entire country. Results: Our model-based crude CFR fitted the observed data well throughout the course of the epidemic except for the very early stage in Gyeongsangbuk-do; this was partially due to the reporting delay. Our estimates of the risk of death in Gyeongsangbuk-do reached 25.9% (95% CrI: 19.6%-33.6%), 20.8% (95% CrI: 18.1%-24.0%) in Daegu and 1.7% (95% CrI: 1.1%-2.5%) in other regions, whereas the national estimate was 10.2% (95% CrI: 9.0%-11.5%). Conclusions: The latest estimates of CFR of COVID-19 in Korea are considerably high, even with the early implementation of public health interventions including widespread testing, social distancing, and delayed school openings. Geographic differences in the CFR are likely influenced by clusters tied to hospitals and nursing homes.

The recent spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) exemplifies the critical need for accurate and rapid diagnostic assays to prompt clinical and public health interventions. Currently, several quantitative reverse-transcription polymerase chain reaction (qRT-PCR) assays are being used by clinical, research, and public health laboratories. However, it is currently unclear if results from different tests are comparable. Our goal was to evaluate the primer-probe sets used in four common diagnostic assays available on the World Health Organization (WHO) website. To facilitate this effort, we generated RNA transcripts to be used as assay standards and distributed them to other laboratories for internal validation. We then used these (1) RNA transcript standards, (2) full-length SARS-CoV-2 RNA, and (3) pre-COVID-19 nasopharyngeal swabs, and (4) clinical samples from COVID-19 patients to determine analytical efficiency and sensitivity of the qRT-PCR primer-probe sets. We show that all primer-probe sets can be used to detect SARS-CoV-2, but there are clear differences in the ability to differentiate between true negatives and positives with low amounts of virus. We found that several primer-probe sets cross-react with SARS-CoV-2-negative nasopharyngeal swabs. However, background cross-reactivity by the 2019-nCoV_N2 set issued by the US Centers for Disease Control and Prevention did not interfere with outcomes of the combined 9N19 and 9N29 assay when testing COVID-19 clinical samples. Our findings characterize the limitations of currently used primer-probe sets and can assist other laboratories in selecting appropriate assays for the detection of SARS-CoV-2.

COVID-19 has become a global pandemic. However, the impact of the public health interventions in China needs to be evaluated. We established a SEIRD model to simulate the transmission trend of China. In addition, the reduction of the reproductive number was estimated under the current forty public health interventions policies. Furthermore, the infection curve, daily transmission replication curve, and the trend of cumulative confirmed cases were used to evaluate the effects of the public health interventions. Our results showed that the SEIRD curve model we established had a good fit and the basic reproductive number is 3.38 (95% CI, 3.25–3.48). The SEIRD curve show a small difference between the simulated number of cases and the actual number; the correlation index (H2) is 0.934, and the reproductive number (R) has been reduced from 3.38 to 0.5 under the current forty public health interventions policies of China. The actual growth curve of new cases, the virus infection curve, and the daily transmission replication curve were significantly going down under the current public health interventions. Our results suggest that the current public health interventions of China are effective and should be maintained until COVID-19 is no longer considered a global threat.

ObjectivesThe novel coronavirus (2019-nCoV) originating from Wuhan has rapidly spread throughout China. While the origin of the outbreak remains uncertain, accumulating evidence links a wet market in Wuhan for the early spread of 2019-nCoV. Similarly, the influence of the marketplace on the early transmission dynamics is yet to be investigated.MethodsUsing the daily series of 2019-nCov incidenceincluding contact history with the market, we have conducted quantitative modeling analyses to estimate the reproduction numbers (R) for the market-to-human and human-to-human transmission together with the reporting probability and the early effects of public health interventions.ResultsOur mean R estimates for China in 2019-2020 are estimated at 0.37 (95%CrI: 0.02-1.78) for market-to-human transmission, and 3.87 (95%CrI: 3.18-4.78) for human-to-human transmission, respectively. Moreover we estimated that the reporting rate cases stemming from market-to-human transmission was 3-31 fold higher than that for cases stemming from human-to-human transmission, suggesting that contact history with the wet market played a key role in identifying 2019-nCov cases.ConclusionsOur findings suggest that the proportions of asymptomatic and subclinical patients constitute a substantial component of the epidemic’s magnitude. Findings suggest that the development of rapid diagnostic tests could help bring the epidemic more rapidly under control.

Background Emergency school closures are often used as public health interventions during infectious disease outbreaks to minimise the spread of infection. However, if children continue mixing with others outside the home during closures, the effect of these measures may be limited. Aim This review aimed to summarise existing literature on children’s activities and contacts made outside the home during unplanned school closures. Methods In February 2020, we searched four databases, MEDLINE, PsycInfo, Embase and Web of Science, from inception to 5 February 2020 for papers published in English or Italian in peer-reviewed journals reporting on primary research exploring children’s social activities during unplanned school closures. Main findings were extracted. Results A total of 3,343 citations were screened and 19 included in the review. Activities and social contacts appeared to decrease during closures, but contact remained common. All studies reported children leaving the home or being cared for by non-household members. There was some evidence that older child age (two studies) and parental disagreement (two studies) with closure were predictive of children leaving the home, and mixed evidence regarding the relationship between infection status and such. Parental agreement with closure was generally high, but some disagreed because of perceived low risk of infection and issues regarding childcare and financial impact. Conclusion Evidence suggests that many children continue to leave home and mix with others during school closures despite public health recommendations to avoid social contact. This review of behaviour during unplanned school closures could be used to improve infectious disease modelling.

As COVID-19 evolves rapidly, the issues the governments of affected countries facing are whether and when to take public health interventions and what levels of strictness of these interventions should be, as well as when the COVID-19 spread reaches the stopping point after interventions are taken. To help governments with policy-making, we developed modified auto-encoders (MAE) method to forecast spread trajectory of Covid-19 of countries affected, under different levels and timing of intervention strategies. Our analysis showed public health interventions should be executed as soon as possible. Delaying intervention 4 weeks after March 8, 2020 would cause the maximum number of cumulative cases of death increase from 7,174 to 133,608 and the ending points of the epidemic postponed from Jun 25 to Aug 22.

We conducted a comparative study of COVID-19 epidemic in three different settings: mainland China, the Guangdong province of China and South Korea, by formulating two disease transmission dynamics models incorporating epidemic characteristics and setting-specific interventions, and fitting the models to multi-source data to identify initial and effective reproduction numbers and evaluate effectiveness of interventions. We estimated the initial basic reproduction number for South Korea, the Guangdong province and mainland China as 2.6 (95% confidence interval (CI): (2.5, 2.7)), 3.0 (95%CI: (2.6, 3.3)) and 3.8 (95%CI: (3.5,4.2)), respectively, given a serial interval with mean of 5 days with standard deviation of 3 days. We found that the effective reproduction number for the Guangdong province and mainland China has fallen below the threshold 1 since February 8th and 18th respectively, while the effective reproduction number for South Korea remains high, suggesting that the interventions implemented need to be enhanced in order to halt further infections. We also project the epidemic trend in South Korea under different scenarios where a portion or the entirety of the integrated package of interventions in China is used. We show that a coherent and integrated approach with stringent public health interventions is the key to the success of containing the epidemic in China and specially its provinces outside its epicenter, and we show that this approach can also be effective to mitigate the burden of the COVID-19 epidemic in South Korea. The experience of outbreak control in mainland China should be a guiding reference for the rest of the world including South Korea.

Since the first case of Novel Coronavirus (2019-nCov) was identified in December 2019 in Wuhan City, China, the number of cases continues to grow across China and multiple cases have been exported to other countries. The cumulative number of reported deaths is at 637 as of February 7, 2020. Here we statistically estimated the time-delay adjusted death risk for Wuhan as well as for China excluding Wuhan to interpret the current severity of the epidemic in China. We found that the latest estimates of the death risk in Wuhan could be as high as 20% in the epicenter of the epidemic whereas we estimate it ∼1% in the relatively mildly-affected areas. Because the elevated death risk estimates are likely associated with a breakdown of the medical/health system, enhanced public health interventions including social distancing and movement restrictions should be effectively implemented to bring the epidemic under control.

A novel coronavirus (COVID-19) was identified in Wuhan, Hubei Province, China, in December 2019 and has caused over 40,000 cases worldwide to date. Previous studies have supported an epidemiological hypothesis that cold and dry (low absolute humidity) environments facilitate the survival and spread of droplet-mediated viral diseases, and warm and humid (high absolute humidity) environments see attenuated viral transmission (i.e., influenza). How-ever, the role of absolute humidity in transmission of COVID-19 has not yet been established. Here, we examine province-level variability of the basic reproductive numbers of COVID-19 across China and find that changes in weather alone (i.e., increase of temperature and humidity as spring and summer months arrive in the North Hemisphere) will not necessarily lead to declines in COVID-19 case counts without the implementation of extensive public health interventions.

Respiratory illness caused by a novel coronavirus (COVID-19) appeared in China during December 2019. Attempting to contain infection, China banned travel to and from Wuhan city on 23 January and implemented a national emergency response. Here we evaluate the spread and control of the epidemic based on a unique synthesis of data including case reports, human movement and public health interventions. The Wuhan shutdown slowed the dispersal of infection to other cities by an estimated 2.91 days (95%CI: 2.54-3.29), delaying epidemic growth elsewhere in China. Other cities that implemented control measures pre-emptively reported 33.3% (11.1-44.4%) fewer cases in the first week of their outbreaks (13.0; 7.1-18.8) compared with cities that started control later (20.6; 14.5-26.8). Among interventions investigated here, the most effective were suspending intra-city public transport, closing entertainment venues and banning public gatherings. The national emergency response delayed the growth and limited the size of the COVID-19 epidemic and, by 19 February (day 50), had averted hundreds of thousands of cases across China.One sentence summaryTravel restrictions and the national emergency response delayed the growth and limited the size of the COVID-19 epidemic in China.

BackgroundExtended-spectrum betalactamase (ESBL-) producingK. pneumoniaeis one of the most common causes of infections with antimicrobial resistant bacteria worldwide. The spread of colonization of humans with this pathogen is on the rise. The future prevalence of colonization with ESBL-producingK. pneumoniae, and the potential of public health interventions to lower it, remain uncertain.MethodsBased on detailed data on antimicrobial consumption and susceptibility systematically recorded for over 13 years in a Swiss region, we developed a mathematical model toi)reconstruct the observed course of colonization with ESBL-producingK. pneumoniae;andii)to assess the potential impact of public health interventions on future trends in colonization.ResultsSimulated prevalence of colonization with ESBL-producingK. pneumoniaestabilized in the near future when rates of antimicrobial consumption and in-hospital transmission remained stable in the main analyses (simulated prevalence in 2025 was 5.3% (5.0%-9.1%) in hospitals and 2.7% (2.1%-4.6%) in the communityversus5.6% (5.1%-9.5%) and 2.8% (2.2%-5.0%) in 2019). The largest changes in future prevalence were observed in simulations that assumed changes in overall antimicrobial consumption. When overall antimicrobial consumption was set to decrease by 50%, prevalence in 2025 declined by 89% in hospitals and by 84% in the community. A 50% decline in transmission rate within hospitals led to a reduction in prevalence of 43% in hospitals and of 13% in the community by 2025. Prevalence changed much less (≤9%) across scenarios with reduced carbapenem consumption. Assuming higher rates for the contribution from external sources of colonization, led to decreasing estimations of future prevalence in hospitals. While high uncertainty remains on the magnitude of these contribution, the best model fit suggested that as much as 46% (95% CI: 12%-96 %) of observed colonizations could be attributable to sources other than human-to-human transmission within the geographical setting (i.e., non-local transmission).ConclusionsThis study suggests that overall antimicrobial consumption will be, by far, the most powerful driver of future prevalence and that a large fraction of colonizations could be attributed to non-local transmission.