Dependence-Robust Confidence Intervals for Capture-Recapture Surveys.

Capture-recapture (CRC) surveys are used to estimate the size of a population whose members cannot be enumerated directly. CRC surveys have been used to estimate the number of Coronavirus Disease 2019 (COVID-19) infections, people who use drugs, sex workers, conflict casualties, and trafficking victims. When k-capture samples are obtained, counts of unit captures in subsets of samples are represented naturally by a 2k contingency table in which one element-the number of individuals appearing in none of the samples-remains unobserved. In the absence of additional assumptions, the population size is not identifiable (i.e., point identified). Stringent assumptions about the dependence between samples are often used to achieve point identification. However, real-world CRC surveys often use convenience samples in which the assumed dependence cannot be guaranteed, and population size estimates under these assumptions may lack empirical credibility. In this work, we apply the theory of partial identification to show that weak assumptions or qualitative knowledge about the nature of dependence between samples can be used to characterize a nontrivial confidence set for the true population size. We construct confidence sets under bounds on pairwise capture probabilities using two methods: test inversion bootstrap confidence intervals and profile likelihood confidence intervals. Simulation results demonstrate well-calibrated confidence sets for each method. In an extensive real-world study, we apply the new methodology to the problem of using heterogeneous survey data to estimate the number of people who inject drugs in Brussels, Belgium.

Geographic and temporal trends in fentanyl-detected deaths in Connecticut, 2009-2019.

PURPOSE: Since 2012 fentanyl-detected fatal overdoses have risen from 4% of all fatal overdoses in Connecticut to 82% in 2019. We aimed to investigate the geographic and temporal trends in fentanyl-detected deaths in Connecticut during 2009-2019. METHODS: Data on the dates and locations of accidental/undetermined opioid-detected fatalities were obtained from Connecticut Office of the Chief Medical Examiner. Using a Bayesian space-time binomial model, we estimated spatiotemporal trends in the proportion of fentanyl-detected deaths. RESULTS: During 2009-2019, a total of 6,632 opioid deaths were identified. Among these, 3234 (49%) were fentanyl-detected. The modeled spatial patterns suggested that opioid deaths in northeastern Connecticut had higher probability of being fentanyl-detected, while New Haven and its neighboring towns and the southwestern region of Connecticut, primarily Greenwich, had a lower risk. Model estimates also suggested fentanyl-detected deaths gradually overtook the preceding non-fentanyl opioid-detected deaths across Connecticut. The estimated temporal trend showed the probability of fentanyl involvement increased substantially since 2014. CONCLUSIONS: Our findings suggest that geographic variation exists in the probability of fentanyl-detected deaths, and areas at heightened risk are identified. Further studies are warranted to explore potential factors contributing to the geographic heterogeneity and continuing dispersion of fentanyl-detected deaths in Connecticut.

Randomized controlled trials of biomarker targets.

INTRODUCTION: Randomized controlled trials are used to estimate the causal effect of a treatment on a health outcome of interest in a patient population. Often the specified treatment in a randomized controlled trial is a medical intervention-such as a drug or procedure-experienced directly by the patient. Sometimes the "treatment" in a randomized controlled trial is a target-such as a goal biomarker measurement-that the patient's physician attempts to reach using available medications or procedures. Large randomized controlled trials of biomarker targets are common in clinical research, and trials have been conducted to compare targets in the management of hypertension, diabetes, anemia, and acute respiratory distress syndrome. However, different randomized controlled trials intended to evaluate the same biomarker targets have produced conflicting recommendations, and meta-analyses that aggregate results of trials of biomarker targets have been inconclusive. METHODS: We use causal reasoning to explain why randomized controlled trials of biomarker targets can arrive at conflicting or misleading conclusions. We describe four key threats to the validity of trials of targets: (1) intention-to-treat analysis can be misleading when a direct effect of target assignment on the outcome exists due to lack of blinding; (2) incomparability in results across trials of targets; (3) time-varying adaptive treatment strategies; and (4) Goodhart's law, "when a measure becomes a target, it ceases to be a good measure." RESULTS: We illustrate these findings using evidence from 15 randomized controlled trials of blood pressure targets for management of hypertension. Randomized trials of blood pressure targets exhibit substantial variation in the trial patient populations and antihypertensives used to achieve the blood pressure targets assigned in the trials. The trials did not compare or account for time-varying treatment strategies used to reach the randomized targets. Possible "off-target" effects of antihypertensive medications needed to reach lower blood pressure targets may explain the absence of a clear benefit from intensive blood pressure control. DISCUSSION: Researchers should critically assess meta-analyses of trials of targets for variation in the types, distributions, and off-target effects of therapies studied. Trial investigators should release detailed information about the biomarker targets compared in new randomized trials, as well as confounders, treatments delivered, and outcomes. New randomized controlled trials should experimentally compare treatment algorithms incorporating biomarkers, rather than targets alone. Causal inference methodology that adjusts for time-varying confounding should be used to compare time-varying treatment strategies in observational settings.

Evaluating the Mediating Role of Recall of Intervention Knowledge in the Relationship Between a Peer-Driven Intervention and HIV Risk Behaviors Among People Who Inject Drugs.

Peer-driven interventions can be effective in reducing HIV injection risk behaviors among people who inject drugs (PWID). We employed a causal mediation framework to examine the mediating role of recall of intervention knowledge in the relationship between a peer-driven intervention and subsequent self-reported HIV injection-related risk behavior among PWID in the HIV Prevention Trials Network (HPTN) 037 study. For each intervention network, the index participant received training at baseline to become a peer educator, while non-index participants and all participants in the control networks received only HIV testing and counseling; recall of intervention knowledge was measured at the 6-month visit for each participant, and each participant was followed to ascertain HIV injection-related risk behaviors at the 12-month visit. We used inverse probability weighting to fit marginal structural models to estimate the total effect (TE) and controlled direct effect (CDE) of the intervention on the outcome. The proportion eliminated (PE) by intervening to remove mediation by the recall of intervention knowledge was computed. There were 385 participants (47% in intervention networks) included in the analysis. The TE and CDE risk ratios for the intervention were 0.47 [95% confidence interval (CI): 0.28, 0.78] and 0.73 (95% CI: 0.26, 2.06) and the PE was 49%. Compared to participants in the control networks, the peer-driven intervention reduced the risk of HIV injection-related risk behavior by 53%. The mediating role of recall of intervention knowledge accounted for less than 50% of the total effect of the intervention, suggesting that other potential causal pathways between the intervention and the outcome, such as motivation and skill, self-efficacy, social norms and behavior modeling, should be considered in future studies.

Randomization for the susceptibility effect of an infectious disease intervention.

Randomized trials of infectious disease interventions, such as vaccines, often focus on groups of connected or potentially interacting individuals. When the pathogen of interest is transmissible between study subjects, interference may occur: individual infection outcomes may depend on treatments received by others. Epidemiologists have defined the primary parameter of interest-called the "susceptibility effect"-as a contrast in infection risk under treatment versus no treatment, while holding exposure to infectiousness constant. A related quantity-the "direct effect"-is defined as an unconditional contrast between the infection risk under treatment versus no treatment. The purpose of this paper is to show that under a widely recommended randomization design, the direct effect may fail to recover the sign of the true susceptibility effect of the intervention in a randomized trial when outcomes are contagious. The analytical approach uses structural features of infectious disease transmission to define the susceptibility effect. A new probabilistic coupling argument reveals stochastic dominance relations between potential infection outcomes under different treatment allocations. The results suggest that estimating the direct effect under randomization may provide misleading conclusions about the effect of an intervention-such as a vaccine-when outcomes are contagious. Investigators who estimate the direct effect may wrongly conclude an intervention that protects treated individuals from infection is harmful, or that a harmful treatment is beneficial.

Sociodemographic characteristics and HIV risk behaviors of native-born and displaced Syrian men and transgender women who have sex with men in Lebanon.

HIV rates among men and transgender women who have sex with men (MTWSM) in Lebanon are consistent with a concentrated epidemic. Geopolitical and social circumstances leave these communities vulnerable to HIV spread. To document this risk encountered by Lebanese native and displaced Syrian MTWSM, participants, recruited by respondent driven sampling beginning with Syrian seeds, completed a survey with questions covering sociodemographic, behavioral, medical, and stigma, followed by opt-out HIV testing. Analyses included descriptive statistics and linear regression to differentiate between native Lebanese and Syrians who migrated after the onset of the civil war to identify correlations among sociodemographic factors, stigma, and risk behavior as a function of country of birth. Experienced and internalized stigmas were higher in the Syrian born MTWSM and correlated with elements of HIV risk. Combatting the intersectional stigmas of Syrian MTWSM in Lebanon would be most beneficial in mitigating HIV risk for these individuals.

RESUMEN: Las tasas de VIH entre hombres y mujeres transgénero que tienen sexo con hombres (HMTSH) en el Líbano son consistentes con una epidemia concentrada. Las circunstancias geopolíticas y sociales dejan a estas comunidades vulnerables a la propagación del VIH. Para documentar este riesgo al que se enfrentan los HMTSH nativos libaneses y HMTSH sirios desplazados, los participantes, reclutados mediante un muestreo impulsado por los encuestados que comenzó con semillas sirias, completaron una encuesta con preguntas que cubrían aspectos sociodemográficos, conductuales, médicos y de estigma, seguidas de una prueba de VIH de exclusión voluntaria. Los análisis incluyeron estadísticas descriptivas y regresión lineal para diferenciar entre libaneses nativos y sirios que emigraron después del inicio de la guerra civil para identificar correlaciones entre factores sociodemográficos, estigma y comportamiento de riesgo como función del país de nacimiento. Los estigmas experimentados e internalizados fueron más altos en los HMTSH nacidos en Siria y se correlacionaron con elementos de riesgo de VIH. Combatir los estigmas interseccionales de los HMTSH sirios en el Líbano sería lo más beneficioso para mitigar el riesgo de VIH para estos individuos.

SARS-CoV-2 Attack Rate and Population Immunity in Southern New England, March 2020 to May 2021.

Importance: In emergency epidemic and pandemic settings, public health agencies need to be able to measure the population-level attack rate, defined as the total percentage of the population infected thus far. During vaccination campaigns in such settings, public health agencies need to be able to assess how much the vaccination campaign is contributing to population immunity; specifically, the proportion of vaccines being administered to individuals who are already seropositive must be estimated. Objective: To estimate population-level immunity to SARS-CoV-2 through May 31, 2021, in Rhode Island, Massachusetts, and Connecticut. Design, Setting, and Participants: This observational case series assessed cases, hospitalizations, intensive care unit occupancy, ventilator occupancy, and deaths from March 1, 2020, to May 31, 2021, in Rhode Island, Massachusetts, and Connecticut. Data were analyzed from July 2021 to November 2021. Exposures: COVID-19-positive test result reported to state department of health. Main Outcomes and Measures: The main outcomes were statistical estimates, from a bayesian inference framework, of the percentage of individuals as of May 31, 2021, who were (1) previously infected and vaccinated, (2) previously uninfected and vaccinated, and (3) previously infected but not vaccinated. Results: At the state level, there were a total of 1 160 435 confirmed COVID-19 cases in Rhode Island, Massachusetts, and Connecticut. The median age among individuals with confirmed COVID-19 was 38 years. In autumn 2020, SARS-CoV-2 population immunity (equal to the attack rate at that point) in these states was less than 15%, setting the stage for a large epidemic wave during winter 2020 to 2021. Population immunity estimates for May 31, 2021, were 73.4% (95% credible interval [CrI], 72.9%-74.1%) for Rhode Island, 64.1% (95% CrI, 64.0%-64.4%) for Connecticut, and 66.3% (95% CrI, 65.9%-66.9%) for Massachusetts, indicating that more than 33% of residents in these states were fully susceptible to infection when the Delta variant began spreading in July 2021. Despite high vaccine coverage in these states, population immunity in summer 2021 was lower than planned owing to an estimated 34.1% (95% CrI, 32.9%-35.2%) of vaccines in Rhode Island, 24.6% (95% CrI, 24.3%-25.1%) of vaccines in Connecticut, and 27.6% (95% CrI, 26.8%-28.6%) of vaccines in Massachusetts being distributed to individuals who were already seropositive. Conclusions and Relevance: These findings suggest that future emergency-setting vaccination planning may have to prioritize high vaccine coverage over optimized vaccine distribution to ensure that sufficient levels of population immunity are reached during the course of an ongoing epidemic or pandemic.

Vaccination with BNT162b2 reduces transmission of SARS-CoV-2 to household contacts in Israel.

The effectiveness of vaccines against COVID-19 on the individual level is well established. However, few studies have examined vaccine effectiveness against transmission. We used a chain binomial model to estimate the effectiveness of vaccination with BNT162b2 [Pfizer-BioNTech messenger RNA (mRNA)-based vaccine] against household transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in Israel before and after emergence of the B.1.617.2 (Delta) variant. Vaccination reduced susceptibility to infection by 89.4% [95% confidence interval (CI): 88.7 to 90.0%], whereas vaccine effectiveness against infectiousness given infection was 23.0% (95% CI: -11.3 to 46.7%) during days 10 to 90 after the second dose, before 1 June 2021. Total vaccine effectiveness was 91.8% (95% CI: 88.1 to 94.3%). However, vaccine effectiveness is reduced over time as a result of the combined effect of waning of immunity and emergence of the Delta variant.

Impact of close interpersonal contact on COVID-19 incidence: Evidence from 1 year of mobile device data.

Close contact between people is the primary route for transmission of SARS-CoV-2, the virus that causes coronavirus disease 2019 (COVID-19). We quantified interpersonal contact at the population level using mobile device geolocation data. We computed the frequency of contact (within 6 feet) between people in Connecticut during February 2020 to January 2021 and aggregated counts of contact events by area of residence. When incorporated into a SEIR-type model of COVID-19 transmission, the contact rate accurately predicted COVID-19 cases in Connecticut towns. Contact in Connecticut explains the initial wave of infections during March to April, the drop in cases during June to August, local outbreaks during August to September, broad statewide resurgence during September to December, and decline in January 2021. The transmission model fits COVID-19 transmission dynamics better using the contact rate than other mobility metrics. Contact rate data can help guide social distancing and testing resource allocation.

COVID-19 Testing and Case Rates and Social Contact Among Residential College Students in Connecticut During the 2020-2021 Academic Year.

Importance: During the 2020-2021 academic year, many institutions of higher education reopened to residential students while pursuing strategies to mitigate the risk of SARS-CoV-2 transmission on campus. Reopening guidance emphasized polymerase chain reaction or antigen testing for residential students and social distancing measures to reduce the frequency of close interpersonal contact, and Connecticut colleges and universities used a variety of approaches to reopen campuses to residential students. Objective: To characterize institutional reopening strategies and COVID-19 outcomes in 18 residential college and university campuses across Connecticut. Design, Setting, and Participants: This retrospective cohort study used data on COVID-19 testing and cases and social contact from 18 college and university campuses in Connecticut that had residential students during the 2020-2021 academic year. Exposures: Tests for COVID-19 performed per week per residential student. Main Outcomes and Measures: Cases per week per residential student and mean (95% CI) social contact per week per residential student. Results: Between 235 and 4603 residential students attended the fall semester across each of 18 institutions of higher education in Connecticut, with fewer residential students at most institutions during the spring semester. In census block groups containing residence halls, the fall student move-in resulted in a 475% (95% CI, 373%-606%) increase in mean contact, and the spring move-in resulted in a 561% (95% CI, 441%-713%) increase in mean contact compared with the 7 weeks prior to move-in. The association between test frequency and case rate per residential student was complex; institutions that tested students infrequently detected few cases but failed to blunt transmission, whereas institutions that tested students more frequently detected more cases and prevented further spread. In fall 2020, each additional test per student per week was associated with a decrease of 0.0014 cases per student per week (95% CI, -0.0028 to -0.00001). Conclusions and Relevance: The findings of this cohort study suggest that, in the era of available vaccinations and highly transmissible SARS-CoV-2 variants, colleges and universities should continue to test residential students and use mitigation strategies to control on-campus COVID-19 cases.

SARS-CoV-2 attack rate and population immunity in southern New England, March 2020 - May 2021.

Estimating an infectious disease attack rate requires inference on the number of reported symptomatic cases of a disease, the number of unreported symptomatic cases, and the number of asymptomatic infections. Population-level immunity can then be estimated as the attack rate plus the number of vaccine recipients who had not been previously infected; this requires an estimate of the fraction of vaccines that were distributed to seropositive individuals. To estimate attack rates and population immunity in southern New England, we fit a validated dynamic epidemiological model to case, clinical, and death data streams reported by Rhode Island, Massachusetts, and Connecticut for the first 15 months of the COVID-19 pandemic, from March 1 2020 to May 31 2021. This period includes the initial spring 2020 wave, the major winter wave of 2020-2021, and the lagging wave of lineage B.1.1.7(Alpha) infections during March-April 2021. In autumn 2020, SARS-CoV-2 population immunity (equal to the attack rate at that point) in southern New England was still below 15%, setting the stage for a large winter wave. After the roll-out of vaccines in early 2021, population immunity in many states was expected to approach 70% by spring 2021, with more than half of this immune population coming from vaccinations. Our population immunity estimates for May 31 2021 are 73.4% (95% CrI: 72.9% - 74.1%) for Rhode Island, 64.1% (95% CrI: 64.0% - 64.4%) for Connecticut, and 66.3% (95% CrI: 65.9% - 66.9%) for Massachusetts, indicating that >33% of southern Englanders were still susceptible to infection when the Delta variant began spreading in July 2021. Despite high vaccine coverage in these states, population immunity in summer 2021 was lower than planned due to 34% (Rhode Island), 25% (Connecticut), and 28% (Massachusetts) of vaccine distribution going to seropositive individuals. Future emergency-setting vaccination planning will likely have to consider over-vaccination as a strategy to ensure that high levels of population immunity are reached during the course of an ongoing epidemic.

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.

Identification of causal intervention effects under contagion.

Defining and identifying causal intervention effects for transmissible infectious disease outcomes is challenging because a treatment - such as a vaccine - given to one individual may affect the infection outcomes of others. Epidemiologists have proposed causal estimands to quantify effects of interventions under contagion using a two-person partnership model. These simple conceptual models have helped researchers develop causal estimands relevant to clinical evaluation of vaccine effects. However, many of these partnership models are formulated under structural assumptions that preclude realistic infectious disease transmission dynamics, limiting their conceptual usefulness in defining and identifying causal treatment effects in empirical intervention trials. In this paper, we propose causal intervention effects in two-person partnerships under arbitrary infectious disease transmission dynamics, and give nonparametric identification results showing how effects can be estimated in empirical trials using time-to-infection or binary outcome data. The key insight is that contagion is a causal phenomenon that induces conditional independencies on infection outcomes that can be exploited for the identification of clinically meaningful causal estimands. These new estimands are compared to existing quantities, and results are illustrated using a realistic simulation of an HIV vaccine trial.

Impact of close interpersonal contact on COVID-19 incidence: evidence from one year of mobile device data.

Close contact between people is the primary route for transmission of SARS-CoV-2, the virus that causes coronavirus disease 2019 (COVID-19). We sought to quantify interpersonal contact at the population-level by using anonymized mobile device geolocation data. We computed the frequency of contact (within six feet) between people in Connecticut during February 2020 - January 2021. Then we aggregated counts of contact events by area of residence to obtain an estimate of the total intensity of interpersonal contact experienced by residents of each town for each day. When incorporated into a susceptible-exposed-infective-removed (SEIR) model of COVID-19 transmission, the contact rate accurately predicted COVID-19 cases in Connecticut towns during the timespan. The pattern of contact rate in Connecticut explains the large initial wave of infections during March-April, the subsequent drop in cases during June-August, local outbreaks during August-September, broad statewide resurgence during September-December, and decline in January 2021. Contact rate data can help guide public health messaging campaigns to encourage social distancing and in the allocation of testing resources to detect or prevent emerging local outbreaks more quickly than traditional case investigation. ONE SENTENCE SUMMARY: Close interpersonal contact measured using mobile device location data explains dynamics of COVID-19 transmission in Connecticut during the first year of the pandemic.

Predicting daily COVID-19 case rates from SARS-CoV-2 RNA concentrations across a diversity of wastewater catchments.

We assessed the relationship between municipality COVID-19 case rates and SARS-CoV-2 concentrations in the primary sludge of corresponding wastewater treatment facilities. Over 1700 daily primary sludge samples were collected from six wastewater treatment facilities with catchments serving 18 cities and towns in the State of Connecticut, USA. Samples were analyzed for SARS-CoV-2 RNA concentrations during a 10 month time period that overlapped with October 2020 and winter/spring 2021 COVID-19 outbreaks in each municipality. We fit lagged regression models to estimate reported case rates in the six municipalities from SARS-CoV-2 RNA concentrations collected daily from corresponding wastewater treatment facilities. Results demonstrate the ability of SARS-CoV-2 RNA concentrations in primary sludge to estimate COVID-19 reported case rates across treatment facilities and wastewater catchments, with coverage probabilities ranging from 0.94 to 0.96. Lags of 0 to 1 days resulted in the greatest predictive power for the model. Leave-one-out cross validation suggests that the model can be broadly applied to wastewater catchments that range in more than one order of magnitude in population served. The close relationship between case rates and SARS-CoV-2 concentrations demonstrates the utility of using primary sludge samples for monitoring COVID-19 outbreak dynamics. Estimating case rates from wastewater data can be useful in locations with limited testing availability, testing disparities, or delays in individual COVID-19 testing programs.

Vaccination with BNT162b2 reduces transmission of SARS-CoV-2 to household contacts in Israel.

The individual-level effectiveness of vaccines against clinical disease caused by SARS-CoV-2 is well-established. However, few studies have directly examined the effect of COVID-19 vaccines on transmission. We quantified the effectiveness of vaccination with BNT162b2 (Pfizer-BioNTech mRNA-based vaccine) against household transmission of SARS-CoV-2 in Israel. We fit two time-to-event models - a mechanistic transmission model and a regression model - to estimate vaccine effectiveness against susceptibility to infection and infectiousness given infection in household settings. Vaccine effectiveness against susceptibility to infection was 80-88%. For breakthrough infections among vaccinated individuals, the vaccine effectiveness against infectiousness was 41-79%. The overall vaccine effectiveness against transmission was 88.5%. Vaccination provides substantial protection against susceptibility to infection and slightly lower protection against infectiousness given infection, thereby reducing transmission of SARS-CoV-2 to household contacts. ONE-SENTENCE SUMMARY: Vaccination reduced both the rate of infection with SARS-CoV-2 and transmission to household contacts in Israel.

Violence and Discrimination Against Men Who Have Sex With Men in Lebanon: The Role of International Displacement and Migration.

Homosexuality is illegal in Lebanon and men who have sex with men (MSM) may experience discrimination. Displaced Syrians, who currently comprise approximately 20% of Lebanon's population, also face discrimination. Individuals who are members of both groups may experience heightened levels of discrimination and abuse. In partnership with local nongovernmental organizations serving the community, we recruited N = 292 MSM in Beirut, Lebanon. Participants were interviewed about experiences of violence and discrimination in the context of a larger health behavior survey, and all were offered anonymous HIV testing. Responses were analyzed using the framework of intersectionality, combining regression, geographical mapping of reported experiences, and network analysis of the participant recruitment pattern. MSM, born outside of Lebanon, who are primarily from Syria, face higher levels of discrimination and violence than native-born MSM (71% vs. 32% reporting at least one type of discrimination or violence). Socioeconomic status is also associated with discrimination and violence overall, and among native- and foreign-born MSM. Experiences vary by town and neighborhood, and are highly correlated between recruiting and recruited participants.These results highlight health risks faced by foreign-born MSM in Lebanon.

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

To support public health policymakers in Connecticut, we developed a 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, as well as estimates of important features of disease transmission, public behavior, healthcare response, and clinical progression of disease. In this paper, we describe a transmission model developed to meet the changing requirements of public health policymakers and officials in Connecticut from March 2020 to February 2021. We outline the model design, implementation and calibration, and describe how projections and estimates were used to support decision-making in Connecticut throughout the first year of the pandemic. We calibrated this model to data on deaths and hospitalizations, 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 time-varying 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 describe methodology for producing projections of epidemic evolution under uncertain future scenarios, as well as analytical tools for estimating epidemic features that are difficult to measure directly, such as cumulative incidence and the effects of non-pharmaceutical interventions. 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 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.

Estimates of people who injected drugs within the last 12 months in Belgium based on a capture-recapture and multiplier method.

BACKGROUND: For Belgium, available estimates of the number of people who inject drugs (PWID) are based on data from more than fifteen years ago and apply only to those who report ever injecting drugs. As a result, no reliable baseline data exist to determine the scale of services for PWID. METHODS: We obtained pseudo-anonymized identifier information from treatment and harm reduction service providers and a fieldwork study between February and April 2019 in Brussels. We estimated the number of PWID, defined as people who injected within the last 12 months, in Brussels using capture-recapture (CRC) methodology. To obtain national estimates, we scaled the proportion of PWID in Brussels to the total number of this population in Belgium based on two existing drug treatment registers, which were then multiplied with the result of the CRC. RESULTS: The total population of PWID is estimated to be 703 (95 %CI 538-935) for Brussels and between 6620 (95 %CI 4711 - 8576) and 7018 (95 %CI 4794 - 9527) for Belgium. CONCLUSIONS: These estimates provide crucial information to ensure that services to PWID are adequately maintained. They clearly indicate the need to maximize efforts to achieve the targets set by WHO for 2030 on the provision of 300 sterile needles and syringes per PWID per year, a 90 % reduction of new HCV infections, and a 65 % reduction of liver-related mortality.