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Undetected infection and delayed isolation of infected individuals are key factors driving the monkeypox virus (now termed mpox virus or MPXV) outbreak. To enable earlier detection of MPXV infection, we developed an image-based deep convolutional neural network (named MPXV-CNN) for the identification of the characteristic skin lesions caused by MPXV. We assembled a dataset of 139,198 skin lesion images, split into training/validation and testing cohorts, comprising non-MPXV images (n = 138,522) from eight dermatological repositories and MPXV images (n = 676) from the scientific literature, news articles, social media and a prospective cohort of the Stanford University Medical Center (n = 63 images from 12 patients, all male). In the validation and testing cohorts, the sensitivity of the MPXV-CNN was 0.83 and 0.91, the specificity was 0.965 and 0.898 and the area under the curve was 0.967 and 0.966, respectively. In the prospective cohort, the sensitivity was 0.89. The classification performance of the MPXV-CNN was robust across various skin tones and body regions. To facilitate the usage of the algorithm, we developed a web-based app by which the MPXV-CNN can be accessed for patient guidance. The capability of the MPXV-CNN for identifying MPXV lesions has the potential to aid in MPXV outbreak mitigation., Stanford Data Science and Biomedical Informatics Training Program at Stanford 2T15LM007033, Spanish Government RTI-2018-101674-B-I00 PID2021-128317OB-I00, J.de Andalucia P20-00163, Fulbright Spanish Commission, United States Department of Health & Human Services, National Institutes of Health (NIH) - USA DP2AI171011 5R25AI147369-03, German Federal Ministry for Economic Affairs and Climate Action (BMWi) BMWi 01MK21009E, Berlin Institute of Health, German Research Foundation (DFG) German Federal Ministry for Economic Affairs and Climate Action and Studienstiftung des deutschen Volkes (German Academic Scholarship Foundation), Charite-Universitaetsmedizin Berlin

Background: \ud The emergence of increasingly antimicrobial-resistant Salmonella enterica serovar Typhi (S Typhi) threatens to undermine effective treatment and control. Understanding where antimicrobial resistance in S Typhi is emerging and spreading is crucial towards formulating effective control strategies.\ud \ud Methods: \ud In this genomic epidemiology study, we sequenced the genomes of 3489 S Typhi strains isolated from prospective enteric fever surveillance studies in Nepal, Bangladesh, Pakistan, and India (between 2014 and 2019), and combined these with a global collection of 4169 S Typhi genome sequences isolated between 1905 and 2018 to investigate the temporal and geographical patterns of emergence and spread of antimicrobial-resistant S Typhi. We performed non-parametric phylodynamic analyses to characterise changes in the effective population size of fluoroquinolone-resistant, extensively drug-resistant (XDR), and azithromycin-resistant S Typhi over time. We inferred timed phylogenies for the major S Typhi sublineages and used ancestral state reconstruction methods to estimate the frequency and timing of international and intercontinental transfers.\ud \ud Findings: \ud Our analysis revealed a declining trend of multidrug resistant typhoid in south Asia, except for Pakistan, where XDR S Typhi emerged in 2016 and rapidly replaced less-resistant strains. Mutations in the quinolone-resistance determining region (QRDR) of S Typhi have independently arisen and propagated on at least 94 occasions, nearly all occurring in south Asia. Strains with multiple QRDR mutations, including triple mutants with high-level fluoroquinolone resistance, have been increasing in frequency and displacing strains with fewer mutations. Strains containing acrB mutations, conferring azithromycin resistance, emerged in Bangladesh around 2013 and effective population size of these strains has been steadily increasing. We found evidence of frequent international (n=138) and intercontinental transfers (n=59) of antimicrobial-resistant S Typhi, followed by local expansion and replacement of drug-susceptible clades.\ud \ud Interpretation: \ud Independent acquisition of plasmids and homoplastic mutations conferring antimicrobial resistance have occurred repeatedly in multiple lineages of S Typhi, predominantly arising in south Asia before spreading to other regions.\ud \ud Funding: \ud Bill & Melinda Gates Foundation.

Background There is high risk of transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in congregate settings, including shelters. This study describes a coronavirus disease 2019 (COVID-19) outbreak and corresponding reported symptomatology at a shelter in Toronto. Methods This clinical and epidemiologic analysis focuses on a COVID-19 outbreak at a dedicated refugee shelter in downtown Toronto. All adult residents on site at the shelter were offered SARS-CoV-2 testing on Apr. 20, 2020. At the time of testing, residents were screened for 3 typical COVID-19 symptoms (fever, cough and shortness of breath). Among those who tested positive, a more comprehensive clinical assessment was conducted 1 day after testing and a standardized 15-item symptom screen was administered by telephone 14 days after testing. We report rates of positive test results and clinical symptoms with each assessment interval. Results Of the 63 adult residents on site at the shelter, 60 agreed to be tested. Among those tested, 41.7% (n = 25) were positive for SARS-CoV-2 infection. Of those who tested positive (n = 25), 20.0% (n = 5) reported fever, cough or shortness of breath at the time of testing. On more detailed assessment 1 day later, 70.8% (17/24) reported a broader range of symptoms. During the 14 days after testing, 87.5% (21/24) reported symptoms of infection. Interpretation We found a high rate of SARS-CoV-2 infection in this shelter population. Our study underscores the high risk of SARS-CoV-2 transmission in congregate living settings and the importance of mobilizing timely testing and management of symptomatic, paucisymptomatic and asymptomatic residents in shelters.

KEY POINTS The coronavirus disease 2019 (COVID-19) pandemic has required governments around the world to institute severe physical-distancing measures to reduce the spread of the virus in order to protect public health and ensure health care system capacity. Mitigation measures in many countries

Background As rates of international travel increase, more individuals are at risk of travel-acquired infections (TAIs). We aimed to review all microbiologically confirmed cases of malaria, dengue, chikungunya, and enteric fever (Salmonella enterica serovar Typhi/Paratyphi) in Ontario, Canada between 2008-2020 to identify high-resolution geographical clusters that could be targeted for pre-travel prevention. Methods Retrospective cohort study of over 174,000 unique tests for the four above TAIs from Public Health Ontario Laboratories. Test-level data were processed to calculate annual case counts and crude population-standardized incidence ratios (SIRs) at the forward sortation area (FSA) level. Moran’s I statistic was used to test for global spatial autocorrelation. Smoothed SIRs and 95% posterior credible intervals (CIs) were estimated using a spatial Bayesian hierarchical model, which accounts for statistical instability and uncertainty in small-area incidence. Posterior CIs were used to identify high- and low-risk areas, which were described using sociodemographic data from the 2016 Census. Finally, a second model was used to estimate the association between drivetime to the nearest travel clinic and risk of TAI within high-risk areas. Results There were 5962 cases of the four TAIs across Ontario over the study period. Smoothed FSA-level SIRs are shown in Figure 1a, with an inset for the Greater Toronto Area (GTA) in 1b. There was spatial clustering of TAIs (Moran’s I=0.61, p< 2.2e-16). Identified high- and low-risk areas are shown in panels c and d. Compared to low-risk areas, high-risk areas were significantly more likely to have higher proportions of immigrants (p< 0.0001), lower household after-tax income (p=0.04), more university education (p< 0.0001), and were less knowledgeable of English/French (p< 0.0001). In the high-risk GTA, each minute increase in drivetime to the closest travel clinic was associated with a 4% reduction in TAI risk (95% CI 2 - 6%). Bayesian hierarchical model (BHM) smoothed standardized incidence ratios (SIRs) for travel-acquired infections (TAIs) and estimated risk levels (a and c) with insets for the Greater Toronto Area (b and d). High-risk areas are defined as those with smoothed SIR 95% CIs greater than 2, and low-risk areas with smoothed SIR 95% CIs less than 0.25. Conclusion Urban neighbourhoods in the GTA had elevated risks of becoming ill with TAIs. However, geographic proximity to a travel clinic was not associated with an area-level risk reduction in TAI, suggesting other barriers to seeking and adhering to pre-travel advice. Disclosures Isaac Bogoch, MD, MSc, BlueDot (Consultant)National Hockey League Players' Association (Consultant) Andrea Boggild, MSc MD DTMH FRCPC, Nothing to disclose Shaun Morris, MD, MPH, DTM&H, FRCPC, FAAP, GSK (Speaker's Bureau)Pfizer (Advisor or Review Panel member)Pfizer (Grant/Research Support)

Scientists across disciplines, policymakers, and journalists have voiced frustration at the unprecedented polarization and misinformation around coronavirus disease 2019 (COVID-19) pandemic. Several false dichotomies have been used to polarize debates while oversimplifying complex issues. In this comprehensive narrative review, we deconstruct six common COVID-19 false dichotomies, address the evidence on these topics, identify insights relevant to effective pandemic responses, and highlight knowledge gaps and uncertainties. The topics of this review are: 1) Health and lives vs. economy and livelihoods, 2) Indefinite lockdown vs. unlimited reopening, 3) Symptomatic vs. asymptomatic severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, 4) Droplet vs. aerosol transmission of SARS-CoV-2, 5) Masks for all vs. no masking, and 6) SARS-CoV-2 reinfection vs. no reinfection. We discuss the importance of multidisciplinary integration (health, social, and physical sciences), multilayered approaches to reducing risk (“Emmentaler cheese model”), harm reduction, smart masking, relaxation of interventions, and context-sensitive policymaking for COVID-19 response plans. We also address the challenges in understanding the broad clinical presentation of COVID-19, SARS-CoV-2 transmission, and SARS-CoV-2 reinfection. These key issues of science and public health policy have been presented as false dichotomies during the pandemic. However, they are hardly binary, simple, or uniform, and therefore should not be framed as polar extremes. We urge a nuanced understanding of the science and caution against black-or-white messaging, all-or-nothing guidance, and one-size-fits-all approaches. There is a need for meaningful public health communication and science-informed policies that recognize shades of gray, uncertainties, local context, and social determinants of health.

ImportanceReliable estimates of COVID-19 mortality are crucial to aid control strategies and to assess the effectiveness of interventions.ObjectiveProject COVID-19 mortality trends to October 1, 2020, in 12 countries or regions that constitute >90% of the global COVID-19 deaths reported as of April 12, 2020.Design, Setting, and ParticipantsThe Global COVID-19 Assessment of Mortality (GCAM) is an open, transparent, and continuously updated (www.cghr.org/covid) statistical model that combines actual COVID-19 mortality counts with Bayesian inference to forecast COVID-19 deaths, the date of peak deaths, and the duration of excess mortality. The analyses covered a total of 700 million population above age 20 in 12 countries or regions: USA; Italy; Spain; France; UK; Iran; Belgium; a province of China (Hubei, which accounted for 90% of reported Chinese deaths); Germany; the Netherlands; Switzerland; and Canada; and six US states: New York, New Jersey, Michigan, Louisiana, California, and Washington.ResultsForecasted deaths across the 12 current high-burden countries sum 167,000 to 593,000 (median 253,000). The trajectory of US deaths (49,000-249,000 deaths; median 86,000)—over half of which are expected in states beyond the initial six states analysed in this study—will have the greatest impact on the eventual total. Mortality ranges are 25,000-109,000 (median 46,000) in the UK; 23,000-31,000 (median 26,000) in Italy; 21,000-37,000 (median 26,000) in France and 21,000-32,000 (median 25,000) in Spain. Estimates are most precise for Hubei, China—where the epidemic curve is complete—and least precise in California, where it is ongoing. New York has the highest cumulative median mortality rate per million (1135), about 12-fold that of Germany. Mortality trajectories are notably flatter in Germany, California, and Washington State, each of which took physical distancing and testing strategies seriously. Using past country-specific mortality as a guide, GCAM predicts surge capacity needs, reaching more than twice existing capacity in a number of places., In every setting, the results might be sensitive to undercounts of COVID-19 deaths, which are already apparent.Conclusion and RelevanceMortality from COVID-19 will be substantial across many settings, even in the best case scenario. GCAM will provide continually updated and increasingly precise estimates as the pandemic progresses.The coronavirus disease (COVID-19) pandemic has already caused over 115,000 deaths, with global deaths doubling every week.1-3Mortality is less biased than case reporting, which is affected by testing policies. However, the daily reporting of COVID-19 deaths is already known to undercount actual deaths, varying over time and place.4-6Reliable estimates of total COVID-19 mortality, the date of peak deaths, and of the duration of excess mortality are crucial to aid responses to the current and potential future pandemics. We have developed the Global COVID-19 Assessment of Mortality (GCAM), a statistical model to project COVID-19 mortality trends to October 1 2020 in 12 countries or regions that constitute >90% of the global COVID-19 deaths reported as of April 12th. We report also on six US states that account for 70% of the American totals to date (Supplementary Appendix).1We quantify the COVID-19 mortality trajectory ranges in each setting. A semi-automated website (www.cghr.org/covid) provides daily updates. GCAM is open, transparent, and uses a reasonably simple method that employs publicly reported mortality data to make plausible projections. The method is designed to improve as more mortality data become available over longer time periods.

Background: Canada had substantial mortality from COVID in the first SARS-CoV-2 wave, mostly in nursing homes. Efforts to stem the pandemic (including vaccination, now being introduced) can benefit from direct understanding of the relationship of age-specific mortality to SARS-CoV-2 seroprevalence and from information on asymptomatic infection. Methods: The Action to Beat Coronavirus (Ab-C) study surveyed a reasonably representative sample of 19,994 adult Canadians about COVID symptoms and analyzed IgG antibodies against SARS-CoV-2 from self-collected dried blood spots (DBS) in 8,967 adults. A sensitive and specific chemiluminescence ELISA detected IgG to the spike trimer. We compared seroprevalence to deaths to establish infection fatality rates (IFRs) and used mortality data to estimate infection levels in nursing home residents. Findings: The best estimate (high specificity) of adult seroprevalence nationally is 1.7%, but as high as 3.5% (high sensitivity) depending on assay cutoffs. The highest prevalence was in Ontario (2.4-3.9%), the most populous province, and in younger adults aged 18-39 years (2.5-4.4%). Based on mortality, we estimated 13-16% of nursing home residents became infected. The first viral wave infected 0.54-1.08 million adult Canadians, half of whom were 70 times higher than comparably-aged adults living in the community. Seropositivity correlated with COVID symptoms, particularly during March. Asymptomatic adults constituted about a quarter of definite seropositives, with a greater proportion in the elderly. Interpretation: Canada had relatively low infection prevalence and low IFRs in the community, but not in nursing homes, during the first viral wave. The Ab-C study demonstrates the practicability of using self-collected DBS for antibody testing in national surveys to monitor the pandemic and vaccine-induced immunity in the population. Funding Statement: Pfizer Global Medical, Unity Health Foundation, Canadian COVID-19 Immunity Task Force Declaration of Interests: We declare no competing interests. Ethics Approval Statement: Ethics approval was provided by St. Michael's Institutional Review Board. REB 20-107