Epidemic Trajectories Research Articles

Incorporating temporal distribution of population-level viral load enables real-time estimation of COVID-19 transmission

Many locations around the world have used real-time estimates of the time-varying effective reproductive number ({R}_{t}) of COVID-19 to provide evidence of transmission intensity to inform control strategies. Estimates of {R}_{t} are typically based on statistical models applied to case counts and typically suffer lags of more than a week because of the latent period and reporting delays. Noting that viral loads tend to decline over time since illness onset, analysis of the distribution of viral loads among confirmed cases can provide insights into epidemic trajectory. Here, we analyzed viral load data on confirmed cases during two local epidemics in Hong Kong, identifying a strong correlation between temporal changes in the distribution of viral loads (measured by RT-qPCR cycle threshold values) and estimates of {R}_{t} based on case counts. We demonstrate that cycle threshold values could be used to improve real-time {R}_{t} estimation, enabling more timely tracking of epidemic dynamics.

Pandemic Parallels: Common Threads between the emergence of SARS-CoV-2 and HIV

PurposeThis analysis explored the parallels between the emergence of human immunodeficiency virus (HIV) and its associated disease (acquired immune deficiency syndrome [AIDS]), and SARS-CoV-2 and its associated disease (Coronavirus disease 2019 [COVID-19]) in order to highlight common patterns that enabled the epidemics of novel pathogens.Methods & MaterialsOur laboratory developed a core set of ten questions that focused on common features found in major disease epidemics, including the affected populations, the response parameters and dynamics of governments, the natural source of the infectious agent, and the impacts of epidemics on societies as a whole. We utilized contemporary accounts including news coverage, written descriptions, documentary accounts, and primary literatures to determine full answers to the core questions.ResultsEight of the ten questions identified commonalities between the AIDS and COVID-19 pandemics. These include slow government policy responses that negatively impacted the timing and the epidemic trajectory, involvement of marginalized populations of societies who were disproportionately affected by the diseases, discovery of existence of persistent economic and social inequalities, and introduction of lifelong morbidities in patients. Most importantly, this analysis found the importance of collaborative, scientifically driven political leadership as evidenced by the improved pace of disease control measures and research for therapeutic and vaccine discovery following adoption of evidence-based policy.ConclusionThis analysis identifies multiple factors that paralleled the trajectory of the HIV/AIDS epidemic and SARS-CoV-2/COVID-19 pandemic. In order to prepare for potential pandemics or large-scale outbreaks in the future, policies mindful of these lessons outlined will help provide guidance for future responses to emerging pathogens.

Bayesian Framework for Multi-Wave COVID-19 Epidemic Analysis Using Empirical Vaccination Data

The COVID-19 pandemic has highlighted the necessity of advanced modeling inference using the limited data of daily cases. Tracking a long-term epidemic trajectory requires explanatory modeling with more complexities than the one with short-time forecasts, especially for the highly vaccinated scenario in the latest phase. With this work, we propose a novel modeling framework that combines an epidemiological model with Bayesian inference to perform an explanatory analysis on the spreading of COVID-19 in Israel. The Bayesian inference is implemented on a modified SEIR compartmental model supplemented by real-time vaccination data and piecewise transmission and infectious rates determined by change points. We illustrate the fitted multi-wave trajectory in Israel with the checkpoints of major changes in publicly announced interventions or critical social events. The result of our modeling framework partly reflects the impact of different stages of mitigation strategies as well as the vaccination effectiveness, and provides forecasts of near future scenarios.

Estimation of the Timing and Intensity of Reemergence of Respiratory Syncytial Virus Following the COVID-19 Pandemic in the US

Respiratory syncytial virus (RSV) is a leading cause of hospitalizations in young children. RSV largely disappeared in 2020 owing to precautions taken because of the COVID-19 pandemic. Estimating the timing and intensity of the reemergence of RSV and the age groups affected is crucial for planning for the administration of prophylactic antibodies and anticipating hospital capacity. To examine the association of different factors, including mitigation strategies, duration of maternal-derived immunity, and importation of external infections, with the dynamics of reemergent RSV epidemics. This simulation modeling study used mathematical models to reproduce the annual epidemics of RSV before the COVID-19 pandemic in New York and California. These models were modified to project the trajectory of RSV epidemics from 2020 to 2025 under different scenarios with varying stringency of mitigation measures for SARS-CoV-2. Simulations also evaluated factors likely to affect the reemergence of RSV epidemics, including introduction of the virus from out-of-state sources and decreased transplacentally acquired immunity in infants. Models using parameters fitted to similar inpatient data sets from Colorado and Florida were used to illustrate these associations in populations with biennial RSV epidemics and year-round RSV circulation, respectively. Statistical analysis was performed from February to October 2021. The primary outcome of this study was defined as the estimated number of RSV hospitalizations each month in the entire population. Secondary outcomes included the age distribution of hospitalizations among children less than 5 years of age, incidence of any RSV infection, and incidence of RSV lower respiratory tract infection. Among a simulated population of 19.45 million people, virus introduction from external sources was associated with the emergence of the spring and summer epidemic in 2021. There was a tradeoff between the intensity of the spring and summer epidemic in 2021 and the intensity of the epidemic in the subsequent winter. Among children 1 year of age, the estimated incidence of RSV hospitalizations was 707 per 100 000 children per year in the 2021 and 2022 RSV season, compared with 355 per 100 000 children per year in a typical RSV season. This simulation modeling study found that virus introduction from external sources was associated with the spring and summer epidemics in 2021. These findings suggest that pediatric departments should be alert to large RSV outbreaks in the coming seasons, the intensity of which could depend on the size of the spring and summer epidemic in that location. Enhanced surveillance is recommended for both prophylaxis administration and hospital capacity management.

Identification of a novel SARS-CoV-2 P.1 sub-lineage in Brazil provides new insights about the mechanisms of emergence of variants of concern.

One of the most remarkable severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants of concern (VOC) features is the significant number of mutations they acquired. However, the specific factors that drove the emergence of such variants since the second half of 2020 are not fully resolved. In this study, we describe a new SARS-CoV-2 P.1 sub-lineage circulating in Brazil, denoted here as Gamma-like-II, that as well as the previously described lineage Gamma-like-I shares several lineage-defining mutations with the VOC Gamma. Reconstructions of ancestor sequences support that most lineage-defining mutations of the Spike (S) protein, including those at the receptor-binding domain (RBD), accumulated at the first P.1 ancestor. In contrast, mutations outside the S protein were mostly fixed at subsequent steps. Our evolutionary analyses estimate that P.1-ancestral strains carrying RBD mutations of concern probably circulated cryptically in the Amazonas for several months before the emergence of the VOC Gamma. Unlike the VOC Gamma, the other P.1 sub-lineages displayed a much more restricted dissemination and accounted for a low fraction (<2 per cent) of SARS-CoV-2 infections in Brazil in 2021. The stepwise diversification of lineage P.1 through multiple inter-host transmissions is consistent with the hypothesis that partial immunity acquired from natural SARS-CoV-2 infections in heavily affected regions might have been a major driving force behind the natural selection of some VOCs. The lag time between the emergence of the P.1 ancestor and the expansion of the VOC Gamma and the divergent epidemic trajectories of P.1 sub-lineages support a complex interplay between the emergence of mutations of concern and viral spread in Brazil.

Real-time monitoring of COVID-19 in Scotland.

To manage the public health risk posed by COVID-19 and assess the impact of interventions, policymakers must be able to closely monitor the epidemic's trajectory. Here we present a simple methodology based on basic surveillance metrics for monitoring the spread of COVID-19 and its burden on health services in Scotland. We examine how the dynamics of the epidemic have changed over time and assess the similarities and differences between metrics. We illustrate how our method has been used throughout the epidemic in Scotland, explore potential biases and conclude that our method has proven to be an effective tool for monitoring the epidemic's trajectory.

Superspreading quantified from bursty epidemic trajectories

The quantification of spreading heterogeneity in the COVID-19 epidemic is crucial as it affects the choice of efficient mitigating strategies irrespective of whether its origin is biological or social. We present a method to deduce temporal and individual variations in the basic reproduction number directly from epidemic trajectories at a community level. Using epidemic data from the 98 districts in Denmark we estimate an overdispersion factor k for COVID-19 to be about 0.11 (95% confidence interval 0.08–0.18), implying that 10 % of the infected cause between 70 % and 87 % of all infections.

Strain Diversity and Spatial Distribution Are Linked to Epidemic Dynamics in Host Populations.

AbstractThe inherently variable nature of epidemics renders predictions of when and where infection is expected to occur challenging. Differences in pathogen strain composition, diversity, fitness, and spatial distribution are generally ignored in epidemiological modeling and are rarely studied in natural populations, yet they may be important drivers of epidemic trajectories. To examine how these factors are linked to epidemics in natural host populations, we collected epidemiological and genetic data from 15 populations of the powdery mildew fungus, Podosphaera plantaginis, on Plantago lanceolata in the Åland Islands, Finland. In each population, we tracked spatiotemporal disease progression throughout one epidemic season and coupled our survey of infection with intensive field sampling of the pathogen. We found that strain composition varied greatly among populations in the landscape. Within populations, strain composition was driven by the sequence of strain activity: early-active strains reached higher abundances, leading to consistent strain compositions over time. Co-occurring strains also varied in their contribution to the growth of the local epidemic, and these fitness inequalities were linked to epidemic dynamics: a higher proportion of hosts became infected in populations containing strains that were more similar in fitness. Epidemic trajectories in the populations were also linked to strain diversity and spatial dynamics: higher infection rates occurred in populations containing higher strain diversity, while spatially clustered epidemics experienced lower infection rates. Together, our results suggest that spatial and/or temporal variation in the strain composition, diversity, and fitness of pathogen populations are important factors generating variation in epidemiological trajectories among infected host populations.

Аssessment of the epidemiological situation and burden of COVID-19 on global, regional and national level

The current pandemic of corona virus disease 2019 (COVID-19) is an infectious disease caused by a newly discovered severe acute respiratory syndrome Coronavirus 2 (SARS-CoV-2). Global COVID-19 burden is characterized by a high variability in death rate across countries. Several possible explanations have been proposed, but it is not clear whether this variability is due to a single predominant factor or instead to multiple causes. The aim of this paper was to present and analyze the epidemiological situation and burden of the COVID-19 pandemic in Kosovo compared to other countries in Europe and the world. Material and methods: Public health approach and desk review were applied to present and analyze the trends over time of selected epidemiological indicators for COVID-19 based on multiple sources of data as well as WHO epidata and literature review. The assessment of the epidemiological situation in EU/EEA member states was done using the surveillance data routinely collected by ECDC. Official medical records from the National Institute of Public Health, other relevant health institutions; State Statistical Office and Ministry of Health in Kosovo were used. Results: According to ECDC as of 11 February 2021, 106,472,660 cases of COVID-19 (in accordance with the applied case definitions and testing strategies in the affected countries) have been reported, including 2,323,103 deaths. COVID-19 cases have been reported from: Africa: 3,673,181 cases, Asia: 20,438,608 cases, Europe: 34,681,426 cases, America: 47,620,931 cases; Oceania: 57,809 cases, Others: 705 cases. Deaths have been reported from: Africa 95,128 deaths, Asia 343,886, America 1,107,066, Europe 775,883, Oceania 1,210 and Other 6 deaths. In Kosovo from 13 March 2020 until 15th February 2021 there have been 64,298 cases of COVID- 19 with 1534 deaths. Absolute values of the indicators remain high in all countries in Europe, including those with stable or decreasing trends in these indicators, suggesting that transmission is still wide spread. The epidemic trajectory over the next months will be determined by the balance of four factors, with many different outcomes possible: the continued scale-up of vaccination, declining seasonality, the spread of new variants and increased behaviors that favor COVID-19 transmission. Conclusion: Findings from this paper can help to develop evidence-based policy interventions in Kosovo for risk assessment of COVID-19 and protection of general population and especially vulnerable groups with higher risk from COVID 19.

Evaluating the reliability of mobility metrics from aggregated mobile phone data as proxies for SARS-CoV-2 transmission in the USA: a population-based study

BackgroundIn early 2020, the response to the SARS-CoV-2 pandemic focused on non-pharmaceutical interventions, some of which aimed to reduce transmission by changing mixing patterns between people. Aggregated location data from mobile phones are an important source of real-time information about human mobility on a population level, but the degree to which these mobility metrics capture the relevant contact patterns of individuals at risk of transmitting SARS-CoV-2 is not clear. In this study we describe changes in the relationship between mobile phone data and SARS-CoV-2 transmission in the USA.MethodsIn this population-based study, we collected epidemiological data on COVID-19 cases and deaths, as well as human mobility metrics collated by advertisement technology that was derived from global positioning systems, from 1396 counties across the USA that had at least 100 laboratory-confirmed cases of COVID-19. We grouped these counties into six ordinal categories, defined by the National Center for Health Statistics (NCHS) and graded from urban to rural, and quantified the changes in COVID-19 transmission using estimates of the effective reproduction number (Rt) between Jan 22 and July 9, 2020, to investigate the relationship between aggregated mobility metrics and epidemic trajectory. For each county, we model the time series of Rt values with mobility proxies.FindingsWe show that the reproduction number is most strongly associated with mobility proxies for change in the travel into counties (0·757 [95% CI 0·689 to 0·857]), but this relationship primarily holds for counties in the three most urban categories as defined by the NCHS. This relationship weakens considerably after the initial 15 weeks of the epidemic (0·442 [−0·492 to −0·392]), consistent with the emergence of more complex local policies and behaviours, including masking.InterpretationOur study shows that the integration of mobility metrics into retrospective modelling efforts can be useful in identifying links between these metrics and Rt. Importantly, we highlight potential issues in the data generation process for transmission indicators derived from mobile phone data, representativeness, and equity of access, which must be addressed to improve the interpretability of these data in public health.FundingThere was no funding source for this study.

Understanding how Victoria, Australia gained control of its second COVID-19 wave

During 2020, Victoria was the Australian state hardest hit by COVID-19, but was successful in controlling its second wave through aggressive policy interventions. We calibrated a detailed compartmental model of Victoria’s second wave to multiple geographically-structured epidemic time-series indicators. We achieved a good fit overall and for individual health services through a combination of time-varying processes, including case detection, population mobility, school closures, physical distancing and face covering usage. Estimates of the risk of death in those aged ≥75 and of hospitalisation were higher than international estimates, reflecting concentration of cases in high-risk settings. We estimated significant effects for each of the calibrated time-varying processes, with estimates for the individual-level effect of physical distancing of 37.4% (95%CrI 7.2−56.4%) and of face coverings of 45.9% (95%CrI 32.9−55.6%). That the multi-faceted interventions led to the dramatic reversal in the epidemic trajectory is supported by our results, with face coverings likely particularly important.

Leveraging of SARS-CoV-2 PCR Cycle Thresholds Values to Forecast COVID-19 Trends.

Introduction: We assessed the usefulness of SARS-CoV-2 RT-PCR cycle thresholds (Ct) values trends produced by the LHUB-ULB (a consolidated microbiology laboratory located in Brussels, Belgium) for monitoring the epidemic's dynamics at local and national levels and for improving forecasting models.Methods: SARS-CoV-2 RT-PCR Ct values produced from April 1, 2020, to May 15, 2021, were compared with national COVID-19 confirmed cases notifications according to their geographical and time distribution. These Ct values were evaluated against both a phase diagram predicting the number of COVID-19 patients requiring intensive care and an age-structured model estimating COVID-19 prevalence in Belgium.Results: Over 155,811 RT-PCR performed, 12,799 were positive and 7,910 Ct values were available for analysis. The 14-day median Ct values were negatively correlated with the 14-day mean daily positive tests with a lag of 17 days. In addition, the 14-day mean daily positive tests in LHUB-ULB were strongly correlated with the 14-day mean confirmed cases in the Brussels-Capital and in Belgium with coinciding start, peak, and end of the different waves of the epidemic. Ct values decreased concurrently with the forecasted phase-shifts of the diagram. Similarly, the evolution of 14-day median Ct values was negatively correlated with daily estimated prevalence for all age-classes.Conclusion: We provide preliminary evidence that trends of Ct values can help to both follow and predict the epidemic's trajectory at local and national levels, underlining that consolidated microbiology laboratories can act as epidemic sensors as they gather data that are representative of the geographical area they serve.

The stochastic dynamics of early epidemics: probability of establishment, initial growth rate, and infection cluster size at first detection.

Emerging epidemics and local infection clusters are initially prone to stochastic effects that can substantially impact the early epidemic trajectory. While numerous studies are devoted to the deterministic regime of an established epidemic, mathematical descriptions of the initial phase of epidemic growth are comparatively rarer. Here, we review existing mathematical results on the size of the epidemic over time, and derive new results to elucidate the early dynamics of an infection cluster started by a single infected individual. We show that the initial growth of epidemics that eventually take off is accelerated by stochasticity. As an application, we compute the distribution of the first detection time of an infected individual in an infection cluster depending on testing effort, and estimate that the SARS-CoV-2 variant of concern Alpha detected in September 2020 first appeared in the UK early August 2020. We also compute a minimal testing frequency to detect clusters before they exceed a given threshold size. These results improve our theoretical understanding of early epidemics and will be useful for the study and control of local infectious disease clusters.

Early intervention is the key to success in COVID-19 control.

New Zealand responded to the COVID-19 pandemic with a combination of border restrictions and an Alert Level (AL) system that included strict stay-at-home orders. These interventions were successful in containing an outbreak and ultimately eliminating community transmission of COVID-19 in June 2020. The timing of interventions is crucial to their success. Delaying interventions may reduce their effectiveness and mean that they need to be maintained for a longer period. We use a stochastic branching process model of COVID-19 transmission and control to simulate the epidemic trajectory in New Zealand's March–April 2020 outbreak and the effect of its interventions. We calculate key measures, including the number of reported cases and deaths, and the probability of elimination within a specified time frame. By comparing these measures under alternative timings of interventions, we show that changing the timing of AL4 (the strictest level of restrictions) has a far greater impact than the timing of border measures. Delaying AL4 restrictions results in considerably worse outcomes. Implementing border measures alone, without AL4 restrictions, is insufficient to control the outbreak. We conclude that the early introduction of stay-at-home orders was crucial in reducing the number of cases and deaths, enabling elimination.

Non-pharmaceutical interventions, vaccination, and the SARS-CoV-2 delta variant in England: a mathematical modelling study

SummaryBackgroundEngland's COVID-19 roadmap out of lockdown policy set out the timeline and conditions for the stepwise lifting of non-pharmaceutical interventions (NPIs) as vaccination roll-out continued, with step one starting on March 8, 2021. In this study, we assess the roadmap, the impact of the delta (B.1.617.2) variant of SARS-CoV-2, and potential future epidemic trajectories.MethodsThis mathematical modelling study was done to assess the UK Government's four-step process to easing lockdown restrictions in England, UK. We extended a previously described model of SARS-CoV-2 transmission to incorporate vaccination and multi-strain dynamics to explicitly capture the emergence of the delta variant. We calibrated the model to English surveillance data, including hospital admissions, hospital occupancy, seroprevalence data, and population-level PCR testing data using a Bayesian evidence synthesis framework, then modelled the potential trajectory of the epidemic for a range of different schedules for relaxing NPIs. We estimated the resulting number of daily infections and hospital admissions, and daily and cumulative deaths. Three scenarios spanning a range of optimistic to pessimistic vaccine effectiveness, waning natural immunity, and cross-protection from previous infections were investigated. We also considered three levels of mixing after the lifting of restrictions.FindingsThe roadmap policy was successful in offsetting the increased transmission resulting from lifting NPIs starting on March 8, 2021, with increasing population immunity through vaccination. However, because of the emergence of the delta variant, with an estimated transmission advantage of 76% (95% credible interval [95% CrI] 69–83) over alpha, fully lifting NPIs on June 21, 2021, as originally planned might have led to 3900 (95% CrI 1500–5700) peak daily hospital admissions under our central parameter scenario. Delaying until July 19, 2021, reduced peak hospital admissions by three fold to 1400 (95% CrI 700–1700) per day. There was substantial uncertainty in the epidemic trajectory, with particular sensitivity to the transmissibility of delta, level of mixing, and estimates of vaccine effectiveness.InterpretationOur findings show that the risk of a large wave of COVID-19 hospital admissions resulting from lifting NPIs can be substantially mitigated if the timing of NPI relaxation is carefully balanced against vaccination coverage. However, with the delta variant, it might not be possible to fully lift NPIs without a third wave of hospital admissions and deaths, even if vaccination coverage is high. Variants of concern, their transmissibility, vaccine uptake, and vaccine effectiveness must be carefully monitored as countries relax pandemic control measures.FundingNational Institute for Health Research, UK Medical Research Council, Wellcome Trust, and UK Foreign, Commonwealth and Development Office.

Nowcasting towards sustainable SARS-CoV-2 endemicity

Nowcasting towards sustainable SARS-CoV-2 endemicity

One year of modeling and forecasting COVID-19 transmission to support policymakers in Connecticut

To support public health policymakers in Connecticut, we developed a flexible county-structured compartmental SEIR-type model of SARS-CoV-2 transmission and COVID-19 disease progression. Our goals were to provide projections of infections, hospitalizations, and deaths, and estimates of important features of disease transmission and clinical progression. In this paper, we outline the model design, implementation and calibration, and describe how projections and estimates were used to meet the changing requirements of policymakers and officials in Connecticut from March 2020 to February 2021. The approach takes advantage of our unique access to Connecticut public health surveillance and hospital data and our direct connection to state officials and policymakers. We calibrated this model to data on deaths and hospitalizations and developed a novel measure of close interpersonal contact frequency to capture changes in transmission risk over time and used multiple local data sources to infer dynamics of time-varying model inputs. Estimated epidemiologic features of the COVID-19 epidemic in Connecticut include the effective reproduction number, cumulative incidence of infection, infection hospitalization and fatality ratios, and the case detection ratio. We conclude with a discussion of the limitations inherent in predicting uncertain epidemic trajectories and lessons learned from one year of providing COVID-19 projections in Connecticut.

Differential impacts of contact tracing and lockdowns on outbreak size in COVID-19 model applied to China

The COVID-19 pandemic has led to widespread attention given to the notions of “flattening the curve” during lockdowns, and successful contact tracing programs suppressing outbreaks. However a more nuanced picture of these interventions’ effects on epidemic trajectories is necessary. By mathematical modeling each as reactive quarantine measures, dependent on current infection rates, with different mechanisms of action, we analytically derive distinct nonlinear effects of these interventions on final and peak outbreak size. We simultaneously fit the model to provincial reported case and aggregated quarantined contact data from China. Lockdowns compressed the outbreak in China inversely proportional to population quarantine rates, revealing their critical dependence on timing. Contact tracing had significantly less impact on final outbreak size, but did lead to peak size reduction. Our analysis suggests that altering the cumulative cases in a rapidly spreading outbreak requires sustained interventions that decrease the reproduction number close to one, otherwise some type of swift lockdown measure may be needed.

The effects of non-pharmaceutical interventions on SARS-CoV-2 transmission in different socioeconomic populations in Kuwait: A modelling study

Background: Aggressive non-pharmaceutical interventions (NPIs) may reduce transmission of SARS-CoV2. The extent to which these interventions are successful in stopping the spread have not been characterized in countries with distinct socioeconomic groups. We compared the effects of a partial lockdown on disease transmission among Kuwaitis (P1) and non-Kuwaitis (P2) living in Kuwait.&#x0D; Methods: We fit a metapopulation Susceptible-Exposed-Infectious-Recovered (SEIR) model to reported cases stratified by two groups to estimate the impact of a lockdown on the effective reproduction number (Re). We estimated the basic reproduction number (R0) for the transmission in each group and simulated the potential trajectories of an outbreak from the first recorded case of community transmission until 12 days after the lockdown. We estimated R­e values of both groups before and after the lockdown, simulated the effect of these values on epidemic curves and explored a range of cross-transmission scenarios.&#x0D; Results: We estimate R0 at 1·06 (95% CI: 1·05-1·28) for P1 and 1·83 (1·58-2·33) for P2. On March 22nd, Re for P1 and P2 are estimated at 1·13 (1·07-1·17) and 1·38 (1·25-1·63) respectively. After the curfew had taken effect, Re for P1 dropped modestly to 1·04 (1·02-1·06) but almost doubled for P2 to 2·47 (1·98-3·45). Our simulated epidemic trajectories show that the partial curfew measure modestly reduced and delayed the height of the peak in P1, yet significantly elevated and hastened the peak in P2. Modest cross-transmission from P2 to P1 elevated the height of the peak in P1 and brought it forward in time closer to the peak of P2. &#x0D; Conclusion: Our results demonstrate that a lockdown can reduce SARS-CoV2 transmission in one subpopulation but accelerate it in another. At the population level, the consequences of lockdowns may vary across the socioeconomic spectrum. Any public health intervention needs to be sensitive to disparities within populations.

Understanding the Potential Impact of Different Drug Properties on Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Transmission and Disease Burden: A Modelling Analysis.

BackgroundThe public health impact of the coronavirus disease 2019 (COVID-19) pandemic has motivated a rapid search for potential therapeutics, with some key successes. However, the potential impact of different treatments, and consequently research and procurement priorities, have not been clear.MethodsUsing a mathematical model of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission, COVID-19 disease and clinical care, we explore the public-health impact of different potential therapeutics, under a range of scenarios varying healthcare capacity, epidemic trajectories; and drug efficacy in the absence of supportive care.ResultsThe impact of drugs like dexamethasone (delivered to the most critically-ill in hospital and whose therapeutic benefit is expected to depend on the availability of supportive care such as oxygen and mechanical ventilation) is likely to be limited in settings where healthcare capacity is lowest or where uncontrolled epidemics result in hospitals being overwhelmed. As such, it may avert 22% of deaths in high-income countries but only 8% in low-income countries (assuming R = 1.35). Therapeutics for different patient populations (those not in hospital, early in the course of infection) and types of benefit (reducing disease severity or infectiousness, preventing hospitalization) could have much greater benefits, particularly in resource-poor settings facing large epidemics.ConclusionsAdvances in the treatment of COVID-19 to date have been focused on hospitalized-patients and predicated on an assumption of adequate access to supportive care. Therapeutics delivered earlier in the course of infection that reduce the need for healthcare or reduce infectiousness could have significant impact, and research into their efficacy and means of delivery should be a priority.