Early warning of vulnerable counties in a pandemic using socio-economic variables

In the U.S. in early 2020, heterogenous and incomplete county-scale data on COVID-19 hindered effective interventions in the pandemic. While numbers of deaths can be used to estimate actual number of infections after a time lag, counties with low death counts early on have considerable uncertainty about true numbers of cases in the future. Here we show that supplementing county-scale mortality statistics with socioeconomic data helps estimate true numbers of COVID-19 infections in low-data counties, and hence provide an early warning of future concern. We fit a LASSO negative binomial regression to select a parsimonious set of five predictive variables from thirty-one county-level covariates. Of these, population density, public transportation use, voting patterns and % African-American population are most predictive of higher COVID-19 death rates. To test the model, we show that counties identified as under-estimating COVID-19 on an early date (April 17) have relatively higher deaths later (July 1) in the pandemic.     

Genetic tracing of HCoV-19 for the re-emerging outbreak of COVID-19 in Beijing,China

The ongoing pandemic of coronavirus disease 2019(COVID-19)caused by a novel severe acute respiratory syndrome coronavirus 2(SARS-CoV-2,also named as 2019-nCoV or HCoV-19)poses an unprecedented threat to public health(Zhu et al.,2020;Wang et al.,2020;Jiang et al.,2020).The novel HCoV-19 virus has rapidly spread into multiple countries across the world since it was first reported in December 2019.The World Health Organization(WHO)declared COVID-19 as a pandemic on 11th March 2020.As of 4th July,over 10 million confirmed COVID-19 cases have been reported in over 200 countries/regions with more than 0.5 million deaths,including 85,287 documented cases and 4,648 deaths in China(WHO,2020a).     

The mitigating effect of masks on the spread of Covid-19

We use US state-level data from early in the pandemic —March 15, 2020 to November 15, 2020— to estimate the effects of mask mandates and compliance with mandates on Covid-19 cases and deaths, conditional on mobility. A one-standard-deviation increase in mobility is associated with a 6 to 20 percent increase in the cases growth rate; a mask mandate can offset about one third of this increase with our most conservative estimates. Also, mask mandates are more effective in states with higher compliance. Given realized mobility, our estimates imply that total infections in the US on November 15, 2020 would have been 23.7 to 30.4 percent lower if a national mask mandate had been enacted on May 15, 2020. This reduction in cases translates to a 25 to 35 percent smaller decline in aggregate hours worked over the same period relative to a 2019 baseline.     

Mass gathering events and the spread of infectious diseases: Evidence from the early growth phase of COVID-19

This paper studies the impact on reported coronavirus 2019 (COVID-19) cases and deaths in Spain resulting from large mass gatherings that occurred from March 6 to March 8, 2020. To study these outcomes, the geographic differences in the planned pre-pandemic major events that took place on these dates were exploited, which is a quasi-random source of variation for identification purposes. We collected daily and detailed information about the number of attendees at football (soccer) and basketball matches in addition to individuals participating in the Women’s Day marches across Spain, which we merged with daily data on reported COVID-19 cases and deaths at the provincial level. Our results reveal evidence of non-negligible COVID-19 cases related to the differences in the percentage of attendees at these major events from March 6 to March 8. In a typical province, approximately 31% of the average daily reported COVID-19 cases per 100,000 inhabitants between mid-March and early April 2020 can be explained by the participation rate in those major events. A back-of-the-envelope calculation suggests that this implies almost five million euros (169,000 euros/day) of additional economic cost in the health system of a typical province with one million inhabitants in the period under consideration. Several mechanisms behind the spread of COVID-19 are also examined.     

COVID-19 and excess mortality in the United States: A county-level analysis

BackgroundCoronavirus Disease 2019 (COVID-19) excess deaths refer to increases in mortality over what would normally have been expected in the absence of the COVID-19 pandemic. Several prior studies have calculated excess deaths in the United States but were limited to the national or state level, precluding an examination of area-level variation in excess mortality and excess deaths not assigned to COVID-19. In this study, we take advantage of county-level variation in COVID-19 mortality to estimate excess deaths associated with the pandemic and examine how the extent of excess mortality not assigned to COVID-19 varies across subsets of counties defined by sociodemographic and health characteristics.Methods and findingsIn this ecological, cross-sectional study, we made use of provisional National Center for Health Statistics (NCHS) data on direct COVID-19 and all-cause mortality occurring in US counties from January 1 to December 31, 2020 and reported before March 12, 2021. We used data with a 10-week time lag between the final day that deaths occurred and the last day that deaths could be reported to improve the completeness of data. Our sample included 2,096 counties with 20 or more COVID-19 deaths. The total number of residents living in these counties was 319.1 million. On average, the counties were 18.7% Hispanic, 12.7% non-Hispanic Black, and 59.6% non-Hispanic White. A total of 15.9% of the population was older than 65 years. We first modeled the relationship between 2020 all-cause mortality and COVID-19 mortality across all counties and then produced fully stratified models to explore differences in this relationship among strata of sociodemographic and health factors. Overall, we found that for every 100 deaths assigned to COVID-19, 120 all-cause deaths occurred (95% CI, 116 to 124), implying that 17% (95% CI, 14% to 19%) of excess deaths were ascribed to causes of death other than COVID-19 itself. Our stratified models revealed that the percentage of excess deaths not assigned to COVID-19 was substantially higher among counties with lower median household incomes and less formal education, counties with poorer health and more diabetes, and counties in the South and West. Counties with more non-Hispanic Black residents, who were already at high risk of COVID-19 death based on direct counts, also reported higher percentages of excess deaths not assigned to COVID-19. Study limitations include the use of provisional data that may be incomplete and the lack of disaggregated data on county-level mortality by age, sex, race/ethnicity, and sociodemographic and health characteristics.ConclusionsIn this study, we found that direct COVID-19 death counts in the US in 2020 substantially underestimated total excess mortality attributable to COVID-19. Racial and socioeconomic inequities in COVID-19 mortality also increased when excess deaths not assigned to COVID-19 were considered. Our results highlight the importance of considering health equity in the policy response to the pandemic.

Andrew Stokes and co-workers report a county-level analysis of excess deaths owing to COVID-19 in the United States.     

Sales of antibiotics and hydroxychloroquine in India during the COVID-19 epidemic: An interrupted time series analysis

BackgroundWe assessed the impact of the coronavirus disease 2019 (COVID-19) epidemic in India on the consumption of antibiotics and hydroxychloroquine (HCQ) in the private sector in 2020 compared to the expected level of use had the epidemic not occurred.Methods and findingsWe performed interrupted time series (ITS) analyses of sales volumes reported in standard units (i.e., doses), collected at regular monthly intervals from January 2018 to December 2020 and obtained from IQVIA, India. As children are less prone to develop symptomatic severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, we hypothesized a predominant increase in non-child-appropriate formulation (non-CAF) sales. COVID-19-attributable changes in the level and trend of monthly sales of total antibiotics, azithromycin, and HCQ were estimated, accounting for seasonality and lockdown period where appropriate. A total of 16,290 million doses of antibiotics were sold in India in 2020, which is slightly less than the amount in 2018 and 2019. However, the proportion of non-CAF antibiotics increased from 72.5% (95% CI: 71.8% to 73.1%) in 2019 to 76.8% (95% CI: 76.2% to 77.5%) in 2020. Our ITS analyses estimated that COVID-19 likely contributed to 216.4 million (95% CI: 68.0 to 364.8 million; P = 0.008) excess doses of non-CAF antibiotics and 38.0 million (95% CI: 26.4 to 49.2 million; P < 0.001) excess doses of non-CAF azithromycin (equivalent to a minimum of 6.2 million azithromycin treatment courses) between June and September 2020, i.e., until the peak of the first epidemic wave, after which a negative change in trend was identified. In March 2020, we estimated a COVID-19-attributable change in level of +11.1 million doses (95% CI: 9.2 to 13.0 million; P < 0.001) for HCQ sales, whereas a weak negative change in monthly trend was found for this drug. Study limitations include the lack of coverage of the public healthcare sector, the inability to distinguish antibiotic and HCQ sales in inpatient versus outpatient care, and the suboptimal number of pre- and post-epidemic data points, which could have prevented an accurate adjustment for seasonal trends despite the robustness of our statistical approaches.ConclusionsA significant increase in non-CAF antibiotic sales, and particularly azithromycin, occurred during the peak phase of the first COVID-19 epidemic wave in India, indicating the need for urgent antibiotic stewardship measures.

Giorgia Sulis and co-workers analyze sales of antimicrobials and hydroxchloroquine in India during 2018-20 to assess possible changes across the COVID-19 epidemic.     

Using test positivity and reported case rates to estimate state-level COVID-19 prevalence and seroprevalence in the United States

Accurate estimates of infection prevalence and seroprevalence are essential for evaluating and informing public health responses and vaccination coverage needed to address the ongoing spread of COVID-19 in each United States (U.S.) state. However, reliable, timely data based on representative population sampling are unavailable, and reported case and test positivity rates are highly biased. A simple data-driven Bayesian semi-empirical modeling framework was developed and used to evaluate state-level prevalence and seroprevalence of COVID-19 using daily reported cases and test positivity ratios. The model was calibrated to and validated using published state-wide seroprevalence data, and further compared against two independent data-driven mathematical models. The prevalence of undiagnosed COVID-19 infections is found to be well-approximated by a geometrically weighted average of the positivity rate and the reported case rate. Our model accurately fits state-level seroprevalence data from across the U.S. Prevalence estimates of our semi-empirical model compare favorably to those from two data-driven epidemiological models. As of December 31, 2020, we estimate nation-wide a prevalence of 1.4% [Credible Interval (CrI): 1.0%-1.9%] and a seroprevalence of 13.2% [CrI: 12.3%-14.2%], with state-level prevalence ranging from 0.2% [CrI: 0.1%-0.3%] in Hawaii to 2.8% [CrI: 1.8%-4.1%] in Tennessee, and seroprevalence from 1.5% [CrI: 1.2%-2.0%] in Vermont to 23% [CrI: 20%-28%] in New York. Cumulatively, reported cases correspond to only one third of actual infections. The use of this simple and easy-to-communicate approach to estimating COVID-19 prevalence and seroprevalence will improve the ability to make public health decisions that effectively respond to the ongoing COVID-19 pandemic.     

Temporal estimates of case-fatality rate for COVID-19 outbreaks in Canada and the United States

BACKGROUND:Estimates of the case-fatality rate (CFR) associated with coronavirus disease 2019 (COVID-19) vary widely in different population settings. We sought to estimate and compare the COVID-19 CFR in Canada and the United States while adjusting for 2 potential biases in crude CFR.METHODS:We used the daily incidence of confirmed COVID-19 cases and deaths in Canada and the US from Jan. 31 to Apr. 22, 2020. We applied a statistical method to minimize bias in the crude CFR by accounting for the survival interval as the lag time between disease onset and death, while considering reporting rates of COVID-19 cases less than 50% (95% confidence interval 10%–50%).RESULTS:Using data for confirmed cases in Canada, we estimated the crude CFR to be 4.9% on Apr. 22, 2020, and the adjusted CFR to be 5.5% (credible interval [CrI] 4.9%–6.4%). After we accounted for various reporting rates less than 50%, the adjusted CFR was estimated at 1.6% (CrI 0.7%–3.1%). The US crude CFR was estimated to be 5.4% on Apr. 20, 2020, with an adjusted CFR of 6.1% (CrI 5.4%–6.9%). With reporting rates of less than 50%, the adjusted CFR for the US was 1.78 (CrI 0.8%–3.6%).INTERPRETATION:Our estimates suggest that, if the reporting rate is less than 50%, the adjusted CFR of COVID-19 in Canada is likely to be less than 2%. The CFR estimates for the US were higher than those for Canada, but the adjusted CFR still remained below 2%. Quantification of case reporting can provide a more accurate measure of the virulence and disease burden of severe acute respiratory syndrome coronavirus 2.

The risk of death associated with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is fundamental to the disease burden imposed by the coronavirus disease 2019 (COVID-19) pandemic. Quantification of this risk can provide critical information on the health and socioeconomic impact of the pandemic and identify population subgroups at highest risk for severe outcomes. The risk of death from a diagnosed infection, often referred to as the case-fatality rate (CFR), is the proportion of people who die from a disease among all those diagnosed with the disease over a certain period.Estimates of the COVID-19 CFR vary in different populations and at different stages of the outbreak, ranging from 0.4% in China¹ to 31.4% in the northwest region of Italy.² From individual-level data for patients in Hubei Province, Mainland China,³ an adjusted CFR of 3.6% (95% confidence interval [CI] 3.6%–3.8%) was estimated. For the outbreak on the Diamond Princess cruise ship, the age-adjusted CFR was estimated at 2.6% (95% CI 0.9%–6.7%) in all age groups but was substantially higher (13.0%, 95% CI 5.2%–26.0%) among those aged 70 years or older.⁴For ongoing outbreaks and especially during the exponential growth phase, the delay between onset of disease and knowledge of the final outcome may result in biased estimates of the CFR.⁵ Furthermore, underestimation of the number of COVID-19 cases will inflate the CFR. Limited ability to test or recognize mildly or moderately symptomatic people in both the United States and Canada has likely led to substantial underestimation of the rate of infection in affected communities.^6,7Given the importance of the CFR in public health planning, we sought to estimate the CFR for ongoing COVID-19 outbreaks in the US and Canada while accounting for preferential ascertainment of severe cases (leading to underestimation) and the lag time between disease onset and death.     

An investigation of spatial-temporal patterns and predictions of the coronavirus 2019 pandemic in Colombia, 2020–2021

Colombia announced the first case of severe acute respiratory syndrome coronavirus 2 on March 6, 2020. Since then, the country has reported a total of 5,002,387 cases and 127,258 deaths as of October 31, 2021. The aggressive transmission dynamics of SARS-CoV-2 motivate an investigation of COVID-19 at the national and regional levels in Colombia. We utilize the case incidence and mortality data to estimate the transmission potential and generate short-term forecasts of the COVID-19 pandemic to inform the public health policies using previously validated mathematical models. The analysis is augmented by the examination of geographic heterogeneity of COVID-19 at the departmental level along with the investigation of mobility and social media trends. Overall, the national and regional reproduction numbers show sustained disease transmission during the early phase of the pandemic, exhibiting sub-exponential growth dynamics. Whereas the most recent estimates of reproduction number indicate disease containment, with R_t<1.0 as of October 31, 2021. On the forecasting front, the sub-epidemic model performs best at capturing the 30-day ahead COVID-19 trajectory compared to the Richards and generalized logistic growth model. Nevertheless, the spatial variability in the incidence rate patterns across different departments can be grouped into four distinct clusters. As the case incidence surged in July 2020, an increase in mobility patterns was also observed. On the contrary, a spike in the number of tweets indicating the stay-at-home orders was observed in November 2020 when the case incidence had already plateaued, indicating the pandemic fatigue in the country.     

Predictive symptoms for COVID-19 in the community: REACT-1 study of over 1 million people

BackgroundRapid detection, isolation, and contact tracing of community COVID-19 cases are essential measures to limit the community spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). We aimed to identify a parsimonious set of symptoms that jointly predict COVID-19 and investigated whether predictive symptoms differ between the B.1.1.7 (Alpha) lineage (predominating as of April 2021 in the US, UK, and elsewhere) and wild type.Methods and findingsWe obtained throat and nose swabs with valid SARS-CoV-2 PCR test results from 1,147,370 volunteers aged 5 years and above (6,450 positive cases) in the REal-time Assessment of Community Transmission-1 (REACT-1) study. This study involved repeated community-based random surveys of prevalence in England (study rounds 2 to 8, June 2020 to January 2021, response rates 22%–27%). Participants were asked about symptoms occurring in the week prior to testing. Viral genome sequencing was carried out for PCR-positive samples with N-gene cycle threshold value < 34 (N = 1,079) in round 8 (January 2021). In univariate analysis, all 26 surveyed symptoms were associated with PCR positivity compared with non-symptomatic people. Stability selection (1,000 penalized logistic regression models with 50% subsampling) among people reporting at least 1 symptom identified 7 symptoms as jointly and positively predictive of PCR positivity in rounds 2–7 (June to December 2020): loss or change of sense of smell, loss or change of sense of taste, fever, new persistent cough, chills, appetite loss, and muscle aches. The resulting model (rounds 2–7) predicted PCR positivity in round 8 with area under the curve (AUC) of 0.77. The same 7 symptoms were selected as jointly predictive of B.1.1.7 infection in round 8, although when comparing B.1.1.7 with wild type, new persistent cough and sore throat were more predictive of B.1.1.7 infection while loss or change of sense of smell was more predictive of the wild type. The main limitations of our study are (i) potential participation bias despite random sampling of named individuals from the National Health Service register and weighting designed to achieve a representative sample of the population of England and (ii) the necessary reliance on self-reported symptoms, which may be prone to recall bias and may therefore lead to biased estimates of symptom prevalence in England.ConclusionsWhere testing capacity is limited, it is important to use tests in the most efficient way possible. We identified a set of 7 symptoms that, when considered together, maximize detection of COVID-19 in the community, including infection with the B.1.1.7 lineage.

Joshua Elliot, Matthew Whitaker, and colleagues investigate predictive symptoms for community COVID-19 cases in the REACT-1 study.     

Re-detectable positive SARS-CoV-2 RNA tests in patients who recovered from COVID-19 with intestinal infection

Dear Editor,Severe acute respiratory syndrome coronavirus 2(SARSCoV-2),a novel coronavirus that causes Coronavirus Disease 2019(COVID-19)(Yang and Wang,2020),has spread to more than 200 countries and infected more than 9,000,000 people as of Jun 2020.Tens of thousands of patients with COVID-19 have recovered and been discharged from hospital.However,there are reports of recovered patients who subsequently tested positive for SARSCoV-2 after discharge(re-detectable positive,RP)(An et al.,2020;Lan et al.,2020),and this has led to increasing focus on the mechanism(s)underlying RP.     

The evolving role of preprints in the dissemination of COVID-19 research and their impact on the science communication landscape

The world continues to face a life-threatening viral pandemic. The virus underlying the Coronavirus Disease 2019 (COVID-19), Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), has caused over 98 million confirmed cases and 2.2 million deaths since January 2020. Although the most recent respiratory viral pandemic swept the globe only a decade ago, the way science operates and responds to current events has experienced a cultural shift in the interim. The scientific community has responded rapidly to the COVID-19 pandemic, releasing over 125,000 COVID-19–related scientific articles within 10 months of the first confirmed case, of which more than 30,000 were hosted by preprint servers. We focused our analysis on bioRxiv and medRxiv, 2 growing preprint servers for biomedical research, investigating the attributes of COVID-19 preprints, their access and usage rates, as well as characteristics of their propagation on online platforms. Our data provide evidence for increased scientific and public engagement with preprints related to COVID-19 (COVID-19 preprints are accessed more, cited more, and shared more on various online platforms than non-COVID-19 preprints), as well as changes in the use of preprints by journalists and policymakers. We also find evidence for changes in preprinting and publishing behaviour: COVID-19 preprints are shorter and reviewed faster. Our results highlight the unprecedented role of preprints and preprint servers in the dissemination of COVID-19 science and the impact of the pandemic on the scientific communication landscape.

An analysis of bioRxiv and medRxiv during the first 10 months of the COVID-19 pandemic reveals that the pandemic has resulted in a cultural shift in the use of preprints for disseminating pandemic-related science.     

Estimating direct and spill-over impacts of political elections on COVID-19 transmission using synthetic control methods

Mass gathering events have been identified as high-risk environments for community transmission of coronavirus disease 2019 (COVID-19). Empirical estimates of their direct and spill-over effects however remain challenging to identify. In this study, we propose the use of a novel synthetic control framework to obtain causal estimates for direct and spill-over impacts of these events. The Sabah state elections in Malaysia were used as an example for our proposed methodology and we investigate the event’s spatial and temporal impacts on COVID-19 transmission. Results indicate an estimated (i) 70.0% of COVID-19 case counts within Sabah post-state election were attributable to the election’s direct effect; (ii) 64.4% of COVID-19 cases in the rest of Malaysia post-state election were attributable to the election’s spill-over effects. Sensitivity analysis was further conducted by examining epidemiological pre-trends, surveillance efforts, varying synthetic control matching characteristics and spill-over specifications. We demonstrate that our estimates are not due to pre-existing epidemiological trends, surveillance efforts, and/or preventive policies. These estimates highlight the potential of mass gatherings in one region to spill-over into an outbreak of national scale. Relaxations of mass gathering restrictions must therefore be carefully considered, even in the context of low community transmission and enforcement of safe distancing guidelines.     

Repurposing FDA-approved drugs for SARS-CoV-2 through an ELISA-based screening for the inhibition of RBD/ACE2 interaction

Dear Editor, The ongoing coronavirus disease 2019(COVID-19)global pandemic is caused by a novel coronavirus,severe acute respiratory syndrome coronavirus 2(SARS-CoV-2),which instigates severe and often fatal symptoms.As of September 4th,2020,more than 26 million cases of COVID-19 and almost 900,000 deaths have been reported to WHO.Based on Kissler and colleagues'modeled projections of future viral transmission scenarios,a resurgence in SARS-CoV-2 could occur over the next five years(Kissler et al.,2020).Research and clinical trials are underway to develop vacci-nes and treatments for COVID-19,but there are currently no specific vaccines or treatments for COVID-19(www.who.int),and therapeutic and prophylactic interventions are urgently needed to combat the outbreak of SARS-CoV-2.Of partic-ular importance is the identification of drugs which are effective,less-intrusive,most socioeconomic,and ready-to-use.     

Bayesian time-varying autoregressive models of COVID-19 epidemics

The COVID-19 pandemic has highlighted the importance of reliable statistical models which, based on the available data, can provide accurate forecasts and impact analysis of alternative policy measures. Here we propose Bayesian time-dependent Poisson autoregressive models that include time-varying coefficients to estimate the effect of policy covariates on disease counts. The model is applied to the observed series of new positive cases in Italy and in the United States. The results suggest that our proposed models are capable of capturing nonlinear growth of disease counts. We also find that policy measures and, in particular, closure policies and the distribution of vaccines, lead to a significant reduction in disease counts in both countries.     

Osteopontin as a biomarker for COVID-19 severity and multisystem inflammatory syndrome in children: A pilot study

This study sought to evaluate the candidacy of plasma osteopontin (OPN) as a biomarker of COVID-19 severity and multisystem inflammatory condition in children (MIS-C) in children. A retrospective analysis of 26 children (0–21 years of age) admitted to Children’s Healthcare of Atlanta with a diagnosis of COVID-19 between March 17 and May 26, 2020 was undertaken. The patients were classified into three categories based on COVID-19 severity levels: asymptomatic or minimally symptomatic (control population, admitted for other non-COVID-19 conditions), mild/moderate, and severe COVID-19. A fourth category of children met the Centers for Disease Control and Prevention''s case definition for MIS-C. Residual blood samples were analyzed for OPN, a marker of inflammation using commercial ELISA kits (R&D), and results were correlated with clinical data. This study demonstrates that OPN levels are significantly elevated in children hospitalized with moderate and severe COVID-19 and MIS-C compared to OPN levels in mild/asymptomatic children. Further, OPN differentiated among clinical levels of severity in COVID-19, while other inflammatory markers including maximum erythrocyte sedimentation rate, C-reactive protein and ferritin, minimum lymphocyte and platelet counts, soluble interleukin-2R, and interleukin-6 did not. We conclude OPN is a potential biomarker of COVID-19 severity and MIS-C in children that may have future clinical utility. The specificity and positive predictive value of this marker for COVID-19 and MIS-C are areas for future larger prospective research studies.     

Modeling the epidemiological impact of the UNAIDS 2025 targets to end AIDS as a public health threat by 2030

BackgroundUNAIDS has established new program targets for 2025 to achieve the goal of eliminating AIDS as a public health threat by 2030. This study reports on efforts to use mathematical models to estimate the impact of achieving those targets.Methods and findingsWe simulated the impact of achieving the targets at country level using the Goals model, a mathematical simulation model of HIV epidemic dynamics that includes the impact of prevention and treatment interventions. For 77 high-burden countries, we fit the model to surveillance and survey data for 1970 to 2020 and then projected the impact of achieving the targets for the period 2019 to 2030. Results from these 77 countries were extrapolated to produce estimates for 96 others. Goals model results were checked by comparing against projections done with the Optima HIV model and the AIDS Epidemic Model (AEM) for selected countries. We included estimates of the impact of societal enablers (access to justice and law reform, stigma and discrimination elimination, and gender equality) and the impact of Coronavirus Disease 2019 (COVID-19). Results show that achieving the 2025 targets would reduce new annual infections by 83% (71% to 86% across regions) and AIDS-related deaths by 78% (67% to 81% across regions) by 2025 compared to 2010. Lack of progress on societal enablers could endanger these achievements and result in as many as 2.6 million (44%) cumulative additional new HIV infections and 440,000 (54%) more AIDS-related deaths between 2020 and 2030 compared to full achievement of all targets. COVID-19–related disruptions could increase new HIV infections and AIDS-related deaths by 10% in the next 2 years, but targets could still be achieved by 2025. Study limitations include the reliance on self-reports for most data on behaviors, the use of intervention effect sizes from published studies that may overstate intervention impacts outside of controlled study settings, and the use of proxy countries to estimate the impact in countries with fewer than 4,000 annual HIV infections.ConclusionsThe new targets for 2025 build on the progress made since 2010 and represent ambitious short-term goals. Achieving these targets would bring us close to the goals of reducing new HIV infections and AIDS-related deaths by 90% between 2010 and 2030. By 2025, global new infections and AIDS deaths would drop to 4.4 and 3.9 per 100,000 population, and the number of people living with HIV (PLHIV) would be declining. There would be 32 million people on treatment, and they would need continuing support for their lifetime. Incidence for the total global population would be below 0.15% everywhere. The number of PLHIV would start declining by 2023.

John Stover and co-workers assess the potential health impacts of UNAIDS’ HIV/AIDS targets.     

From impossible to possible: the lessons from the control of recent COVID-19 outbreaks in China

The COVID-19 pandemic has catastrophically impacted the world. Before the success in vaccination, this virus shows no sign of stop spreading. Nearly all the countries have implemented stringent approaches to slow down the transmission of the virus, but the virus still caused over 2 million deaths and the number is increasing. Therefore, preventing the virus spreading is still necessary to protect most people, especially the ones with pre-conditions. Mainland China has successfully eradicated the COVID-19 virus infection in Wuhan in 2020. After that, several small-scale outbreaks occurred in many cities in China, but none of these COVID-19 virus infections caused the widespread. In this review, we would like to give a detailed presentation of the approaches that were implemented by the China government to suppress the virus spreading by considering the unique characteristics of this virus and the paths of the virus transmission. Both the pros and cons of these strategies will also be analyzed. The experiences and lessons learned during the virus-fighting in China, expectedly, will be a useful source of reference for other regions in overcoming the threat caused by the COVID-19 virus.