Study on Infection Probabilities of COVID-19 by SIR Network

Severe Acute Respiratory Syndrome Coronavirus 2 Related Diseases (COVID-19) is now one of the most challenging and concerning epidemics, which has been affecting the world so much. After that, countries around the world have been actively developing vaccines to deal with the sudden disease. How to carry out more efficient epidemic prevention has also become a problem of our concern. Unlike traditional SIR disease transmission models, network percolation has unique advantages in disease immune modelling, which makes it closer to reality in the simulation. This article introduces the study of SIR percolation network on infection probabilities of COVID-19, and proposes a method to preventing the spread of disease. © 2022 IEEE.

A Contact Matrix-Based Approach for Predicting COVID-19 Using Influenza Data

The global pandemic Corona Virus Disease 2019 (COVID-19) has become one of the deadliest epidemics in human history, bringing enormous harm to human society. To help health policymakers respond to the threat of COVID-19, prediction of outbreaks is needed. Research on COVID-19 prediction usually uses data-driven models and mechanism models. However, in the early stages of the epidemic, there were not enough data to establish a data-driven model. The inadequate understanding of the virus that causes COVID-19, SARS-COV-2, has also led to the inaccuracies of the mechanism model. This has left the government with the toughest Non-pharmaceutical interventions (NPIs) to curb the spread of the virus, such as the lockdown of Wuhan in 2020. Yet man is a social animal, and social relations and interactions are necessary for his existence. The novel coronavirus and containment measures have challenged human and community interactions, affecting the lives of individuals and collective societies. To help governments take appropriate and necessary actions in the early stages of an epidemic, and to mitigate its impact on people's psychology and lives, we used the COVID-19 pandemic as an example to develop a model that uses surveillance data from one epidemic to predict the development trend of another. Based on the fact that both influenza and COVID-19 are transmitted through infectious respiratory droplets, we hypothesized that they may have the same underlying contact structure, and we proposed the influenza data-based COVID-19 prediction (ICP) model. In this model, the underlying contact pattern is firstly inferred by using a singular value decomposition method from influenza surveillance data. Then the contact matrix was used to simulate the influenza virus transmission through close contact of people, and the influenza virus transmission model was established. In order to be able to simulate the spread of COVID-19 virus using influenza transmission models, we used influenza contact matrix and COVID-19 infection data to estimate the risk of a population contracting COVID-19, i.e. force of infection of COVID-19. Finally, we used force of infection and influenza virus transmission model to simulate and predict the spread of COVID-19 in the population. We obtained age-disaggregated influenza and COVID-19 infection data for the United States in 2020, as well as data for Europe, which was not disaggregated by age. We use correlation coefficients as an evaluation indicator, and the final results prove that the predicted value and the actual value are positively correlated. So, the development trend of COVID-19 can be predicted using influenza surveillance data. © 2022 IEEE.

Optimal evaluation of re-opening policies for COVID-19 through the use of metaheuristic schemes.

A new contagious disease or unidentified COVID-19 variants could provoke a new collapse in the global economy. Under such conditions, companies, factories, and organizations must adopt reopening policies that allow their operations to reduce economic effects. Effective reopening policies should be designed using mathematical models that emulate infection chains through individual interactions. In contrast to other modeling approaches, agent-based schemes represent a computational paradigm used to characterize the person-to-person interactions of individuals inside a system, providing accurate simulation results. To evaluate the optimal conditions for a reopening policy, authorities and decision-makers need to conduct an extensive number of simulations manually, with a high possibility of losing information and important details. For this reason, the integration of optimization and simulation of reopening policies could automatically find the realistic scenario under which the lowest risk of infection was attained. In this paper, the metaheuristic technique of the Whale Optimization Algorithm is used to find the solution with the minimal transmission risk produced by an agent-based model that emulates a hypothetical re-opening context. Our scheme finds the optimal results of different generical activation scenarios. The experimental results indicate that our approach delivers practical knowledge and essential estimations for identifying optimal re-opening strategies with the lowest transmission risk.

A numerical study of COVID-19 epidemic model with vaccination and diffusion.

The coronavirus infectious disease (or COVID-19) is a severe respiratory illness. Although the infection incidence decreased significantly, still it remains a major panic for human health and the global economy. The spatial movement of the population from one region to another remains one of the major causes of the spread of the infection. In the literature, most of the COVID-19 models have been constructed with only temporal effects. In this paper, a vaccinated spatio-temporal COVID-19 mathematical model is developed to study the impact of vaccines and other interventions on the disease dynamics in a spatially heterogeneous environment. Initially, some of the basic mathematical properties including existence, uniqueness, positivity, and boundedness of the diffusive vaccinated models are analyzed. The model equilibria and the basic reproductive number are presented. Further, based upon the uniform and non-uniform initial conditions, the spatio-temporal COVID-19 mathematical model is solved numerically using finite difference operator-splitting scheme. Furthermore, detailed simulation results are presented in order to visualize the impact of vaccination and other model key parameters with and without diffusion on the pandemic incidence. The obtained results reveal that the suggested intervention with diffusion has a significant impact on the disease dynamics and its control.

EpiGNN: Exploring Spatial Transmission with Graph Neural Network for Regional Epidemic Forecasting

Epidemic forecasting is the key to effective control of epidemic transmission and helps the world mitigate the crisis that threatens public health. To better understand the transmission and evolution of epidemics, we propose EpiGNN, a graph neural network-based model for epidemic forecasting. Specifically, we design a transmission risk encoding module to characterize local and global spatial effects of regions in epidemic processes and incorporate them into the model. Meanwhile, we develop a Region-Aware Graph Learner (RAGL) that takes transmission risk, geographical dependencies, and temporal information into account to better explore spatial-temporal dependencies and makes regions aware of related regions' epidemic situations. The RAGL can also combine with external resources, such as human mobility, to further improve prediction performance. Comprehensive experiments on five real-world epidemic-related datasets (including influenza and COVID-19) demonstrate the effectiveness of our proposed method and show that EpiGNN outperforms state-of-the-art baselines by 9.48% in RMSE. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Transmission trends of the global COVID-19 pandemic with combined effects of adaptive behaviours and vaccination.

We developed a mechanism model which allows for simulating the novel coronavirus (COVID-19) transmission dynamics with the combined effects of human adaptive behaviours and vaccination, aiming at predicting the end time of COVID-19 infection in global scale. Based on the surveillance information (reported cases and vaccination data) between 22 January 2020 and 18 July 2022, we validated the model by Markov Chain Monte Carlo (MCMC) fitting method. We found that (1) if without adaptive behaviours, the epidemic could sweep the world in 2022 and 2023, causing 3.098 billion of human infections, which is 5.39 times of current number; (2) 645 million people could be avoided from infection due to vaccination; and (3) in current scenarios of protective behaviours and vaccination, infection cases would increase slowly, levelling off around 2023, and it would end completely in June 2025, causing 1.024 billion infections, with 12.5 million death. Our findings suggest that vaccination and the collective protection behaviour remain the key determinants against the global process of COVID-19 transmission.

Testing if higher contact among vaccinated can be a mechanism for observed negative vaccine effectiveness.

Evidence from early observational studies suggested negative vaccine effectiveness (${V}_{Eff}$) for the SARS-CoV-2 Omicron variant. Since true ${V}_{Eff}$ is unlikely to be negative, we explored how differences in contact among vaccinated persons (e.g. potentially from the implementation of vaccine mandates), could lead to observed negative ${V}_{Eff}$. Using an $SEIR$transmission model, we examined how vaccinated contact heterogeneity, defined as an increase in the contact rate only between vaccinated individuals, interacted with two mechanisms of vaccine efficacy: vaccine efficacy against susceptibility (${VE}_S$) and vaccine efficacy against infectiousness (${VE}_I$), to produce underestimated and in some cases, negative measurements of ${V}_{Eff}$. We found that vaccinated contact heterogeneity led to negative estimates when ${VE}_I$, and especially ${VE}_S,$ were low. Moreover, we determined that when contact heterogeneity was very high, ${V}_{Eff}$ could still be underestimated given relatively high vaccine efficacies (0.7) although its effect on ${V}_{Eff}$ was strongly reduced. We also found that this contact heterogeneity mechanism generated a signature temporal pattern: the largest underestimates and negative measurements of ${V}_{Eff}$ occurred during epidemic growth. Overall, our research illustrates how vaccinated contact heterogeneity could have feasibly produced negative measurements during the Omicron period and highlights its general ability to bias observational studies of ${V}_{Eff}$.

A review on indoor airborne transmission of COVID-19– modelling and mitigation approaches

In the past few years, significant efforts have been made to investigate the transmission of COVID-19. This paper provides a review of the COVID-19 airborne transmission modeling and mitigation strategies. The simulation models here are classified into airborne transmission infectious risk models and numerical approaches for spatiotemporal airborne transmissions. Mathematical descriptions and assumptions on which these models have been based are discussed. Input data used in previous simulation studies to assess the dispersion of COVID-19 are extracted and reported. Moreover, measurements performed to study the COVID-19 airborne transmission within indoor environments are introduced to support validations for anticipated future modeling studies. Transmission mitigation strategies recommended in recent studies have been classified to include modifying occupancy and ventilation operations, using filters and air purifiers, installing ultraviolet (UV) air disinfection systems, and personal protection compliance, such as wearing masks and social distancing. The application of mitigation strategies to various building types, such as educational, office, public, residential, and hospital, is reviewed. Recommendations for future works are also discussed based on the current apparent knowledge gaps covering both modeling and mitigation approaches. Our findings show that different transmission mitigation measures were recommended for various indoor environments;however, there is no conclusive work reporting their combined effects on the level of mitigation that may be achieved. Moreover, further studies should be conducted to understand better the balance between approaches to mitigating the viral transmissions in buildings and building energy consumption. © 2022

The Effects of Time Window-Averaged Mobility on Effective Reproduction Number of COVID-19 Viral Variants in Urban Cities.

During epidemics, the estimation of the effective reproduction number (ERN) associated with infectious disease is a challenging topic for policy development and medical resource management. The emergence of new viral variants is common in widespread pandemics including the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). A simple approach is required toward an appropriate and timely policy decision for understanding the potential ERN of new variants is required for policy revision. We investigated time-averaged mobility at transit stations as a surrogate to correlate with the ERN using the data from three urban prefectures in Japan. The optimal time windows, i.e., latency and duration, for the mobility to relate with the ERN were investigated. The optimal latency and duration were 5-6 and 8 days, respectively (the Spearman's ρ was 0.109-0.512 in Tokyo, 0.365-0.607 in Osaka, and 0.317-0.631 in Aichi). The same linear correlation was confirmed in Singapore and London. The mobility-adjusted ERN of the Alpha variant was 15-30%, which was 20-40% higher than the original Wuhan strain in Osaka, Aichi, and London. Similarly, the mobility-adjusted ERN of the Delta variant was 20%-40% higher than that of the Wuhan strain in Osaka and Aichi. The proposed metric would be useful for the proper evaluation of the infectivity of different SARS-CoV-2 variants in terms of ERN as well as the design of the forecasting system.

School and community reopening during the COVID-19 pandemic: a mathematical modelling study.

Operating schools safely during the COVID-19 pandemic requires a balance between health risks and the need for in-person learning. Using demographic and epidemiological data between 31 July and 23 November 2020 from Toronto, Canada, we developed a compartmental transmission model with age, household and setting structure to study the impact of schools reopening in September 2020. The model simulates transmission in the home, community and schools, accounting for differences in infectiousness between adults and children, and accounting for work-from-home and virtual learning. While we found a slight increase in infections among adults (2.2%) and children (4.5%) within the first eight weeks of school reopening, transmission in schools was not the key driver of the virus resurgence in autumn 2020. Rather, it was community spread that determined the outbreak trajectory, primarily due to increases in contact rates among adults in the community after school reopening. Analyses of cross-infection among households, communities and schools revealed that home transmission is crucial for epidemic progression and safely operating schools, while the degree of in-person attendance has a larger impact than other control measures in schools. This study suggests that safe school reopening requires the strict maintenance of public health measures in the community.

Public health impact of UK COVID-19 booster vaccination programs during Omicron predominance.

BACKGROUND: We aimed to estimate the public health impact of booster vaccination against COVID-19 in the UK during an Omicron-predominant period. RESEARCH DESIGN AND METHODS: A dynamic transmission model was developed to compare public health outcomes for actual and alternative UK booster vaccination programs. Input sources were publicly available data and targeted literature reviews. Base case analyses estimated outcomes from the UK's Autumn-Winter 2021-2022 booster program during January-March 2022, an Omicron-predominant period. Scenario analyses projected outcomes from Spring and in Autumn 2022 booster programs over an extended time horizon from April 2022-April 2023, assuming continued Omicron predominance, and explored hypothetical program alternatives with modified eligibility criteria and/or increased uptake. RESULTS: Estimates predicted that the Autumn-Winter 2021-2022 booster program averted approximately 12.8 million cases, 1.1 million hospitalizations, and 290,000 deaths. Scenario analyses suggested that Spring and Autumn 2022 programs would avert approximately 6.2 million cases, 716,000 hospitalizations, and 125,000 deaths; alternatives extending eligibility or targeting risk groups would improve these benefits, and increasing uptake would further strengthen impact. CONCLUSIONS: Boosters were estimated to provide substantial benefit to UK public health during Omicron predominance. Benefits of booster vaccination could be maximized by extending eligibility and increasing uptake.

Virus transmission risk of college students in railway station during Post-COVID-19 era: Combining the social force model and the virus transmission model.

In the post-epidemic era, people's lives are gradually returning to normal, and travel is gradually resuming. The safe evacuation of cross-regional travelers in railway station has also attracted more and more attention, especially the evacuation behavior of college students in railway station. In this paper, considering the pedestrian dynamics mechanism in the emergency evacuation process during the COVID-19 normalized epidemic prevention and control, an Agent-based social force model was established to simulate the activities of college students in railway station. Combined with the virus infection transmission model, Monte Carlo simulation was used to calculate the total exposure time and the number of high-risk exposed people in the railway station evacuation process. In addition, sensitivity analysis was conducted on the total exposure time and the number of high-risk exposed people under 180 combinations of the number of initial infections, social distance, and the proportion of people wearing masks incorrectly. The results show that with the increase of social distances, the total exposure time and the number of high-risk exposures do not always decrease, but increase in some cases. The presence or absence of obstacles in the evacuation scene has no significant difference in the effects on total exposure time and the number of high-risk exposures. During the evacuation behavior of college students in railway station, choosing the appropriate number of lines can effectively reduce the total exposure time and the number of high-risk exposures. Finally, some policy suggestions are proposed to reduce the risk of virus transmission in the railway station evacuation process, such as choosing dynamic and reasonable social distance and the number of queues, and reducing obstacles.

Assessing the mechanism of citywide test-trace-isolate Zero-COVID policy and exit strategy of COVID-19 pandemic.

BACKGROUND: Countries that aimed for eliminating the cases of COVID-19 with test-trace-isolate policy are found to have lower infections, deaths, and better economic performance, compared with those that opted for other mitigation strategies. However, the continuous evolution of new strains has raised the question of whether COVID-19 eradication is still possible given the limited public health response capacity and fatigue of the epidemic. We aim to investigate the mechanism of the Zero-COVID policy on outbreak containment, and to explore the possibility of eradication of Omicron transmission using the citywide test-trace-isolate (CTTI) strategy. METHODS: We develop a compartmental model incorporating the CTTI Zero-COVID policy to understand how it contributes to the SARS-CoV-2 elimination. We employ our model to mimic the Delta outbreak in Fujian Province, China, from September 10 to October 9, 2021, and the Omicron outbreak in Jilin Province, China for the period from March 1 to April 1, 2022. Projections and sensitivity analyses were conducted using dynamical system and Latin Hypercube Sampling/ Partial Rank Correlation Coefficient (PRCC). RESULTS: Calibration results of the model estimate the Fujian Delta outbreak can end in 30 (95% confidence interval CI: 28-33) days, after 10 (95% CI: 9-11) rounds of citywide testing. The emerging Jilin Omicron outbreak may achieve zero COVID cases in 50 (95% CI: 41-57) days if supported with sufficient public health resources and population compliance, which shows the effectiveness of the CTTI Zero-COVID policy. CONCLUSIONS: The CTTI policy shows the capacity for the eradication of the Delta outbreaks and also the Omicron outbreaks. Nonetheless, the implementation of radical CTTI is challenging, which requires routine monitoring for early detection, adequate testing capacity, efficient contact tracing, and high isolation compliance, which constrain its benefits in regions with limited resources. Moreover, these challenges become even more acute in the face of more contagious variants with a high proportion of asymptomatic cases. Hence, in regions where CTTI is not possible, personal protection, public health control measures, and vaccination are indispensable for mitigating and exiting the COVID-19 pandemic.

Public health impact of booster vaccination against COVID-19 in the UK during Delta variant dominance in autumn 2021.

AIM: To evaluate the public health impact of the UK COVID-19 booster vaccination program in autumn 2021, during a period of SARS-CoV-2 Delta variant predominance. MATERIALS AND METHODS: A compartmental Susceptible-Exposed-Infectious-Recovered model was used to compare age-stratified health outcomes for adult booster vaccination versus no booster vaccination in the UK over a time horizon of September-December 2021, when boosters were introduced in the UK and the SARS-CoV-2 Delta variant was predominant. Model input data were sourced from targeted literature reviews and publicly available data. Outcomes were predicted COVID-19 cases, hospitalizations, post-acute sequelae of COVID-19 (PASC) cases, deaths, and productivity losses averted, and predicted healthcare resources saved. Scenario analyses varied booster coverage, virus infectivity and severity, and time horizon parameters. RESULTS: Booster vaccination was estimated to have averted approximately 547,000 COVID-19 cases, 36,000 hospitalizations, 147,000 PASC cases, and 4,200 deaths in the UK between September and December 2021. It saved over 316,000 hospital bed-days and prevented the loss of approximately 16.5 million paid and unpaid patient work days. In a scenario of accelerated uptake, the booster rollout would have averted approximately 3,400 additional deaths and 25,500 additional hospitalizations versus the base case. A scenario analysis assuming four-fold greater virus infectivity and lower severity estimated that booster vaccination would have averted over 105,000 deaths and over 41,000 hospitalizations versus the base case. A scenario analysis assuming pediatric primary series vaccination prior to adult booster vaccination estimated that expanding vaccination to children aged ≥5 years would have averted approximately 51,000 additional hospitalizations and 5,400 additional deaths relative to adult booster vaccination only. LIMITATIONS: The model did not include the wider economic burden of COVID-19, hospital capacity constraints, booster implementation costs, or non-pharmaceutical interventions. CONCLUSIONS: Booster vaccination during Delta variant predominance reduced the health burden of SARS-CoV-2 in the UK, releasing substantial NHS capacity.

Forecasting the dynamic of the COVID-19 pandemic by an adaptive Cauchy Quantum-Behaved Particle Swarm Optimization Algorithm

Accurate identification of parameters is critical to the epidemiological utility of the results obtained from the COVID-19 transmission model. In order to optimize the model parameters, we propose an adaptive Cauchy quantum particle swarm optimization (QPSO) algorithm. We introduce a piecewise Cauchy mutation operator and the mutation probability is adjusted adaptively according to the fitness to enhance the global search ability of QPSO. The experimental results show that the improved QPSO algorithm has higher accuracy than original QPSO and PSO algorithms. © 2022 SPIE.

Discussion on “The timing and effectiveness of implementing mild interventions of COVID-19 in large industrial regions via a synthetic control method” by Tian et al

This article provides an overview and discussion of the recent published paper from Tian et al. on modeling the differences of COVID-19 outbreak between Shenzhen and a synthetic population constructed from 68 US counties. © 2021. Statistics and its Interface. All Rights Reserved.

An agent-based transmission model of COVID-19 for re-opening policy design.

The global pandemic caused by the coronavirus (COVID-19) disease has collapsed the worldwide economy. Elements such as non-obligatory vaccination, new strain variants and lack of discipline to follow social distancing measures suggest the possibility that COVID-19 may continue to exist, exhibiting the behavior of a seasonal disease. As the socio-economic crisis has become unsustainable, all countries are planning strategies to gradually restart their economic and social activities. Initially, several containment measures have been adopted involving social distancing, infection detection tests, and ventilation systems. Despite the implementation of such policies, there exists a lack of evaluation of their performance to reduce the contagion index. This means there are no appropriate indicators to decide which intervention or set of interventions present the most effective result. Under these conditions, the development of models that provide useful information in the design and evaluation of containment measures and re-opening policies is of prime concern. In this paper, a novel approach to model the transmission process of COVID-19 in closed environments is proposed. The proposed model can simulate the effects that result from the complex interaction among individuals when they follow a particular containment measure or re-opening policy. With the proposed model, different hypothetical re-opening policies, that are otherwise impossible to analyze in real conditions, can be tested. Computer experiments demonstrate that the proposed model provides suitable information and realistic predictions, which are appropriate for designing strategies that allow a safe return to economic activities.

Dynamic COVID risk assessment accounting for community virus exposure from a spatial-temporal transmission model

COVID-19 pandemic has caused unprecedented negative impacts on our society, including further exposing inequity and disparity in public health. To study the impact of socioeconomic factors on COVID transmission, we first propose a spatial-temporal model to examine the socioeconomic heterogeneity and spatial correlation of COVID-19 transmission at the community level. Second, to assess the individual risk of severe COVID-19 outcomes after a positive diagnosis, we propose a dynamic, varying-coefficient model that integrates individual-level risk factors from electronic health records (EHRs) with community-level risk factors. The underlying neighborhood prevalence of infections (both symptomatic and pre-symptomatic) predicted from the previous spatial-temporal model is included in the individual risk assessment so as to better capture the background risk of virus exposure for each individual. We design a weighting scheme to mitigate multiple selection biases inherited in EHRs of COVID patients. We analyze COVID transmission data in New York City (NYC, the epicenter of the first surge in the United States) and EHRs from NYC hospitals, where time-varying effects of community risk factors and significant interactions between individual- and community-level risk factors are detected. By examining the socioeconomic disparity of infection risks and interaction among the risk factors, our methods can assist public health decision-making and facilitate better clinical management of COVID patients. © 2021 Neural information processing systems foundation. All rights reserved.

Modeling the effect of Fangcang shelter hospitals on the control of COVID-19 epidemic.

The ongoing COVID-19 pandemic has posed a tremendous threat to the public and health authorities. Wuhan, as one of the cities experiencing the earliest COVID-19 outbreak, has successfully tackled the epidemic finally. The main reason is the implementing of Fangcang shelter hospitals, which rapidly and massively scale the health system's capacity to treat COVID-19 confirmed cases with mild symptoms. To give insights on what degree Fangcang shelter hospitals have contained COVID-19 in Wuhan, we proposed a piecewise smooth model regarding the patient triage scheme and the bed capacities of Fangcang shelter hospitals and designated hospitals. We used data on the cumulative number of confirmed cases, recovered cases, deaths, and data on the number of hospitalized individuals in Fangcang shelter hospitals and designated hospitals in Wuhan to parameterize the targeted model. Our results showed that diminishing the bed capacity or delaying the opening time of Fangcang shelter hospitals, both would result in worsening the epidemic by increasing the total number of infectives and hospitalized individuals and the effective reproduction number R e ( t ) . The findings demonstrated that Fangcang shelter hospitals avoided 17,013 critical infections and 17,823 total infections while it saved 7 days during the process of controlling the effective reproduction number R e ( t ) < 1 . Our study highlighted the critical role of Fangcang shelter hospitals in curbing and eventually stopping COVID-19 outbreak in Wuhan, China. These findings may provide a valuable reference for decision-makers in regarding ramping up the health system capacity to isolate groups of people with mild symptoms in areas of widespread infection.

Potential Effects of the Coronavirus Disease 2019 (COVID-19) Pandemic on Human Immunodeficiency Virus (HIV) Transmission: A Modeling Study in 32 US Cities.

BACKGROUND: The degree to which the 2019 novel coronavirus disease (COVID-19) pandemic will affect the US human immunodeficiency virus (HIV) epidemic is unclear. METHODS: We used the Johns Hopkins Epidemiologic and Economic Model to project HIV infections from 2020 to 2025 in 32 US metropolitan statistical areas (MSAs). We sampled a range of effects of the pandemic on sexual transmission (0-50% reduction), viral suppression among people with HIV (0-40% reduction), HIV testing (0-50% reduction), and pre-exposure prophylaxis use (0-30% reduction), and indexed reductions over time to Google Community Mobility Reports. RESULTS: Simulations projected reported diagnoses would drop in 2020 and rebound in 2021 or 2022, regardless of underlying incidence. If sexual transmission normalized by July 2021 and HIV care normalized by January 2022, we projected 1161 (1%) more infections from 2020 to 2025 across all 32 cities than if COVID-19 had not occurred. Among "optimistic" simulations in which sexual transmission was sharply reduced and viral suppression was maintained we projected 8% lower incidence (95% credible interval: 14% lower to no change). Among "pessimistic" simulations where sexual transmission was largely unchanged but viral suppression fell, we projected 11% higher incidence (1-21% higher). MSA-specific projections are available at www.jheem.org?covid. CONCLUSIONS: The effects of COVID-19 on HIV transmission remain uncertain and differ between cities. Reported diagnoses of HIV in 2020-2021 are likely to correlate poorly with underlying incidence. Minimizing disruptions to HIV care is critical to mitigating negative effects of the COVID-19 pandemic on HIV transmission.