Estimating the impact of COVID-19 vaccine allocation inequities: a modeling study (preprint)

Access to COVID-19 vaccines on the global scale has been drastically impacted by structural socio-economic inequities. Here, we develop a data-driven, age-stratified epidemic model to evaluate the effects of COVID-19 vaccine inequities in twenty lower middle and low income countries (LMIC) sampled from all WHO regions. We focus on the first critical months of vaccine distribution and administration, exploring counterfactual scenarios where we assume the same per capita daily vaccination rate reported in selected high income countries. We estimate that, in this high vaccine availability scenario, more than 50% of deaths (min-max range: 56%-99%) that occurred in the analyzed countries could have been averted. We further consider a scenario where LMIC had similarly early access to vaccine doses as high income countries; even without increasing the number of doses, we estimate an important fraction of deaths (min-max range: 7%-73%) could have been averted. In the absence of equitable allocation, the model suggests that considerable additional non-pharmaceutical interventions would have been required to offset the lack of vaccines (min-max range: 15%-75%). Overall, our results quantify the negative impacts of vaccines inequities and call for amplified global efforts to provide better access to vaccine programs in low and lower middle income countries

Effectiveness of Primary and Booster COVID-19 mRNA Vaccination against Infection Caused by the SARS-CoV-2 Omicron Variant in People with a Prior SARS-CoV-2 Infection (preprint)

Importance: The benefit of primary and booster vaccination in people who experienced prior SARS-CoV-2 infection remains unclear. Objective: To estimate the effectiveness of a primary (two-dose) and booster (third dose) vaccination against Omicron infection among previously infection people. Design: Test-negative case-control study. Setting: Yale New Haven Health System facilities serving southern Connecticut communities. Participants: Vaccine eligible people who received SARS-CoV-2 RT-PCR testing between November 1, 2021, and January 31, 2022. Exposure: COVID-19 mRNA primary and booster vaccination. Main Outcomes and Measures: We conducted two analyses, each with an outcome of Omicron BA.1 variant infection (S-gene target failure defined) and each stratified by prior SARS-CoV-2 infection status. We estimated the effectiveness of primary vaccination during the period before and during booster eligibility (14-149 and [≥]150 days, respectively, after 2nd dose) and of booster vaccination ([≥]14 days after booster dose). To test whether booster vaccination reduced the risk of infection beyond that of the primary series, we compared the odds among boosted and booster eligible people. Results: Overall, 10,676 cases and 119,397 controls were included (median age: cases: 35 years, controls: 39 years). Among cases and controls, 6.1% and 7.8% had a prior infection. The effectiveness of primary vaccination 14-149 days after 2nd dose was 36.1% (95% CI, 7.1-56.1%) and 28.5% (95% CI, 20.0-36.2%) for people with and without prior infection, respectively. The effectiveness of booster vaccination was 45.8% (95% CI, 20.0-63.2%) and 56.9% (95% CI, 52.1-61.2%) in people with and without prior infection, respectively. The odds ratio comparing boosted and booster eligible people with prior infection was 0.83 (95% CI, 0.56-1.23), whereas the odds ratio comparing boosted and booster eligible people without prior infection was 0.51 (95% CI, 0.46-0.56). Conclusions and Relevance: Primary vaccination provided significant but limited protection against Omicron BA.1 infection among people with and without prior infection. While booster vaccination was associated with additional protection in people without prior infection, it was not associated with additional protection among people with prior infection. These findings support primary vaccination in people regardless of prior infection status but suggest that infection history should be considered when evaluating the need for booster vaccination.

Effectiveness of CoronaVac, ChAdOx1, BNT162b2 and Ad26.COV2.S among individuals with prior SARS-CoV-2 infection in Brazil (preprint)

Background. COVID-19 vaccines have proven highly effective among SARS-CoV-2 naive individuals, but their effectiveness in preventing symptomatic infection and severe outcomes among individuals with prior infection is less clear. Methods. Utilizing national COVID-19 notification, hospitalization, and vaccination datasets from Brazil, we performed a case-control study using a test-negative design to assess the effectiveness of four vaccines (CoronaVac, ChAdOx1, Ad26.COV2.S and BNT162b2) among individuals with laboratory-confirmed prior SARS-CoV-2 infection. We matched RT-PCR positive, symptomatic COVID-19 cases with RT-PCR-negative controls presenting with symptomatic illnesses, restricting both groups to tests performed at least 90 days after an initial infection. We used multivariable conditional logistic regression to compare the odds of test positivity, and the odds of hospitalization or death due to COVID-19, according to vaccination status and time since first or second dose of vaccines. Findings. Among individuals with prior SARS-CoV-2 infection, vaccine effectiveness against symptomatic infection [≥] 14 days from vaccine series completion was 39.4% (95% CI 36.1-42.6) for CoronaVac, 56.0% (95% CI 51.4-60.2) for ChAdOx1, 44.0% (95% CI 31.5-54.2) for Ad26.COV2.S, and 64.8% (95% CI 54.9-72.4) for BNT162b2. For the two-dose vaccine series (CoronaVac, ChAdOx1, and BNT162b2), effectiveness against symptomatic infection was significantly greater after the second dose compared with the first dose. Effectiveness against hospitalization or death [≥] 14 days from vaccine series completion was 81.3% (95% CI 75.3-85.8) for CoronaVac, 89.9% (95% CI 83.5-93.8) for ChAdOx1, 57.7% (95% CI -2.6-82.5) for Ad26.COV2.S, and 89.7% (95% CI 54.3-97.7) for BNT162b2.

Change in COVID-19 risk over time following vaccination with CoronaVac: A test-negative case-control study (preprint)

Objective To estimate the change in odds of covid-19 over time following primary series completion of the inactivated whole virus vaccine, CoronaVac (Sinovac Biotech) in Sao Paulo State, Brazil. Design Test negative case-control study. Setting Community testing for covid-19 in Sao Paulo state, Brazil. Participants Adults aged 18-120 years who were residents of Sao Paulo state, without a previous laboratory-confirmed covid-19 infection, who received two doses of CoronaVac, and underwent reverse transcription polymerase chain reaction (RT-PCR) testing for SARS-CoV-2 from 17 January to 30 September 2021. Main outcome measures RT-PCR-confirmed symptomatic covid-19 and associated hospital admissions and deaths. Cases were pair-matched to test-negative controls by age (in 5-year bands), municipality of residence, healthcare worker (HCW) status, and date of RT-PCR test ({+/-}3 days). Conditional logistic regression was adjusted for sex, number of covid-19-associated comorbidities, race, and previous acute respiratory infection. Results From 137,820 eligible individuals, 37,929 cases with symptomatic covid-19 and 25,756 test-negative controls with covid-19 symptoms were formed into 37,929 matched pairs. Adjusted odds ratios of symptomatic covid-19 increased with time since series completion, and this increase was greater in younger individuals, and among HCWs compared to non-HCWs. Adjusted odds ratios of covid-19 hospitalisation or death were significantly increased from 98 days since series completion, compared to individuals vaccinated 14-41 days previously: 1.40 (95% confidence interval 1.09 to 1.79) from 98-125 days, 1.55 (1.16 to 2.07) from 126-153 days, 1.56 (1.12 to 2.18) from 154-181 days, and 2.12 (1.39-3.22) from 182 days. Conclusions In the general population of Sao Paulo state, Brazil, an increase in odds of moderate and severe covid-19 outcomes was observed over time following primary series completion with CoronaVac.

Efficacy Estimates for Various COVID-19 Vaccines: What we Know from the Literature and Reports (preprint)

In this report, we provide summary estimates, from publications and reports, of vaccine efficacy (VE) for the COVID-19 vaccines that are being rolled out on a global scale. We find that, on average, the efficacy against any disease with infection is 85% (95% CI: 71 - 93%) after a full course of vaccination. The VE against severe disease, hospitalization or death averages close to 100%. The average VE against infection, regardless of symptoms, is 84% (95% CI: 70 - 91%). We also find that the average VE against transmission to others for Infected vaccinated people is 54% (95% CI: 38 - 66%). Finally, we prove summary estimates of the VE against any disease with infection for some of the variants of concern (VOC). The average VE for the VOC B.1.1.7, B.1.1.28 (P1) and B.1.351 are 86% (95% CI: 65 - 84%), 61% (95% CI: 43 - 73%) and 56% (95% CI: 29 - 73%), respectively.

Cryptic transmission of SARS-CoV-2 and the first COVID-19 wave in Europe and the United States (preprint)

Given the narrowness of the initial testing criteria, the SARS-CoV-2 virus spread through cryptic transmission in January and February, setting the stage for the epidemic wave experienced in March and April, 2020. We use a global metapopulation epidemic model to provide a mechanistic understanding of the global dynamic underlying the establishment of the COVID-19 pandemic in Europe and the United States (US). The model is calibrated on international case introductions at the early stage of the pandemic. We find that widespread community transmission of SARS-CoV-2 was likely in several areas of Europe and the US by January 2020, and estimate that by early March, only 1 - 3 in 100 SARS-CoV-2 infections were detected by surveillance systems. Modeling results indicate international travel as the key driver of the introduction of SARS-CoV-2 with possible importation and transmission events as early as December, 2019. We characterize the resulting heterogeneous spatio-temporal spread of SARS-CoV-2 and the burden of the first COVID-19 wave (February-July 2020). We estimate infection attack rates ranging from 0.78%-15.2% in the US and 0.19%-13.2% in Europe. The spatial modeling of SARS-CoV-2 introductions and spreading provides insights into the design of innovative, model-driven surveillance systems and preparedness plans that have a broader initial capacity and indication for testing.

Theoretical framework for retrospective studies of the effectiveness of SARS-CoV-2 vaccines (preprint)

Observational studies of the effectiveness of vaccines to prevent COVID-19 are needed to inform real-world use. These are now in planning amid the ongoing rollout of SARS-CoV-2 vaccines globally. While traditional case-control (TCC) and test-negative design (TND) studies feature prominently among strategies used to assess vaccine effectiveness, such studies may encounter important threats to validity. Here we review the theoretical basis for estimation of vaccine direct effects under TCC and TND frameworks, addressing specific natural history parameters of SARS-CoV-2 infection and COVID-19 relevant to these designs. Bias may be introduced by misclassification of cases and controls, particularly when clinical case criteria include common, non-specific indicators of COVID-19. When using diagnostic assays with high analytical sensitivity for SARS-CoV-2 detection, individuals testing positive may be counted as cases even if their symptoms are due to other causes. The TCC may be particularly prone to confounding due to associations of vaccination with healthcare-seeking behavior or risk of infection. The TND reduces but may not eliminate this confounding, for instance if individuals who receive vaccination seek care or testing for less-severe infection. These circumstances indicate the two study designs cannot be applied naively to datasets gathered through public health surveillance or administrative sources. We suggest practical strategies to reduce bias in vaccine effectiveness estimates at the study design and analysis stages.

SARS-CoV-2 shifting transmission dynamics and hidden reservoirs limited the efficacy of public health interventions in Italy (preprint)

We investigated SARS-CoV-2 transmission dynamics in Italy, one of the countries hit hardest by the pandemic, using phylodynamic analysis of viral genetic and epidemiological data. We observed the co-circulation of at least 13 different SARS-CoV-2 lineages over time, which were linked to multiple importations and characterized by large transmission clusters concomitant with a high number of infections. Subsequent implementation of a three-phase nationwide lockdown strategy greatly reduced infection numbers and hospitalizations. Yet we present evidence of sustained viral spread among sporadic clusters acting as "hidden reservoirs" during summer 2020. Mathematical modelling shows that increased mobility among residents eventually catalyzed the coalescence of such clusters, thus driving up the number of infections and initiating a new epidemic wave. Our results suggest that the efficacy of public health interventions is, ultimately, limited by the size and structure of epidemic reservoirs, which may warrant prioritization during vaccine deployment.

Quantifying the importance and location of SARS-CoV-2 transmission events in large metropolitan areas (preprint)

Detailed characterization of SARS-CoV-2 transmission across different settings can help design less disruptive interventions. We used real-time, privacy-enhanced mobility data in the New York City and Seattle metropolitan areas to build a detailed agent-based model of SARS-CoV-2 infection to estimate the where, when, and magnitude of transmission events during the pandemics first wave. We estimate that only 18% of individuals produce most infections (80%), with about 10% of events that can be considered super-spreading events (SSEs). Although mass-gatherings present an important risk for SSEs, we estimate that the bulk of transmission occurred in smaller events in settings like workplaces, grocery stores, or food venues. The places most important for transmission change during the pandemic and are different across cities, signaling the large underlying behavioral component underneath them. Our modeling complements case studies and epidemiological data and indicates that real-time tracking of transmission events could help evaluate and define targeted mitigation policies.

Household transmission of SARS-CoV-2: a systematic review and meta-analysis of secondary attack rate (preprint)

BackgroundSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is spread by direct, indirect, or close contact with infected people via infected respiratory droplets or saliva. Crowded indoor environments with sustained close contact and conversations are a particularly high-risk setting. MethodsWe performed a meta-analysis through July 29, 2020 of SARS-CoV-2 household secondary attack rate (SAR), disaggregating by several covariates (contact type, symptom status, adult/child contacts, contact sex, relationship to index case, index case sex, number of contacts in household, coronavirus). FindingsWe identified 40 relevant published studies that report household secondary transmission. The estimated overall household SAR was 18{middle dot}8% (95% confidence interval [CI]: 15{middle dot}4%-22{middle dot}2%), which is higher than previously observed SARs for SARS-CoV and MERS-CoV. We observed that household SARs were significantly higher from symptomatic index cases than asymptomatic index cases, to adult contacts than children contacts, to spouses than other family contacts, and in households with one contact than households with three or more contacts. InterpretationTo prevent the spread of SARS-CoV-2, people are being asked to stay at home worldwide. With suspected or confirmed infections referred to isolate at home, household transmission will continue to be a significant source of transmission.

Estimating the establishment of local transmission and the cryptic phase of the COVID-19 pandemic in the USA (preprint)

We use a global metapopulation transmission model to study the establishment of sustained and undetected community transmission of the COVID-19 epidemic in the United States. The model is calibrated on international case importations from mainland China and takes into account travel restrictions to and from international destinations. We estimate widespread community transmission of SARS-CoV-2 in February, 2020. Modeling results indicate international travel as the key driver of the introduction of SARS-CoV-2 in the West and East Coast metropolitan areas that could have been seeded as early as late-December, 2019. For most of the continental states the largest contribution of imported infections arrived through domestic travel flows.

The usefulness of SARS-CoV-2 test positive proportion as a surveillance tool (preprint)

Comparison of COVID-19 case numbers over time and between locations is complicated by limits to virologic testing confirm SARS-CoV-2 infection, leading to under-reporting of incidence, and by variations in testing capacity between locations and over time. The proportion of tested individuals who have tested positive (test positive proportion, TPP) can potentially be used to qualitatively assess the testing capacity of a location; a high TPP could provide evidence that too few people are tested, leading to more under-reporting. In this study we propose a simple model for testing in a population experiencing an epidemic of COVID-19, and derive an expression for TPP in terms of well-defined parameters in the model, related to testing and presence of other pathogens causing COVID-19 like symptoms. We use simulations to show situations in which the TPP is higher or lower than we expect based on these parameters, and the effect of testing strategies on the TPP. In our simulations, we find in the absence of dramatic shifts of testing practices in time or between spatial locations, the TPP is positively correlated with the incidence of infection. As a corollary, the TPP can be used to distinguish between a decline in confirmed cases due to decline in incidence (in which case TPP should decline) and a decline in confirmed cases due to testing constraints (in which case TPP should remain constant). We show that the proportion of tested individuals who present COVID-19 like symptoms (test symptomatic proportion, TSP) encodes similar information to the TPP but has different relationships with the testing parameters, and can thus provide additional information regarding dynamic changes in TPP and incidence. Finally, we compare data on confirmed cases and TPP from US states. We conjecture why states may have higher or lower TPP than average. We suggest that collection of symptom status and age/risk category of tested individuals can aid interpretation of changes in TPP and increase the utility of TPP in assessing the state of the pandemic in different locations and times. SummaryO_LIKey question: when can we use the proportion of tests that are positive (test positive proportion, TPP) as an indicator of the burden of infection in a state? C_LIO_LIIf testing strategies are broadly similar between locations and over time, the TPP is positively correlated with incidence rates. C_LIO_LIHowever, changes in testing practices over time and between locations can affect the TPP independently of the number of cases. C_LIO_LIMore testing of asymptomatic individuals, e.g. through population-level testing, lowers the TPP. C_LIO_LIWe can identify locations that have a lower or higher TPP than expected, given how many cases they are reporting. C_LIO_LIEfficient transmission increases detected cases exponentially, resulting in large changes in confirmed cases compared to factors that change linearly. C_LIO_LIData that could aid interpretability of the TPP include: age of individuals who test positive and negative, and other data on testing performed in high-prevalence settings; and symptom status of tested individuals. C_LI

Modeling the impact of social distancing, testing, contact tracing and household quarantine on second-wave scenarios of the COVID-19 epidemic (preprint)

The new coronavirus disease 2019 (COVID-19) has required the implementation of severe mobility restrictions and social distancing measures worldwide. While these measures have been proven effective in abating the epidemic in several countries, it is important to estimate the effectiveness of testing and tracing strategies to avoid a potential second wave of the COVID-19 epidemic. We integrate highly detailed (anonymized, privacy-enhanced) mobility data from mobile devices, with census and demographic data to build a detailed agent-based model to describe the transmission dynamics of SARS-CoV-2 in the Boston metropolitan area. We find that enforcing strict social distancing followed by a policy based on a robust level of testing, contact-tracing and household quarantine, could keep the disease at a level that does not exceed the capacity of the health care system. Assuming the identification of 50% of the symptomatic infections, and the tracing of 40% of their contacts and households, which corresponds to about 9% of individuals quarantined, the ensuing reduction in transmission allows the reopening of economic activities while attaining a manageable impact on the health care system. Our results show that a response system based on enhanced testing and contact tracing can play a major role in relaxing social distancing interventions in the absence of herd immunity against SARS-CoV-2.

Household Secondary Attack Rate of COVID-19 and Associated Determinants (preprint)

Background: As of April 2, 2020, the global reported number of COVID-19 cases has crossed over 1 million with more than 55,000 deaths. The household transmissibility of SARS-CoV-2, the causative pathogen, remains elusive. Methods: Based on a comprehensive contact-tracing dataset from Guangzhou, we estimated both the population-level effective reproductive number and individual-level secondary attack rate (SAR) in the household setting. We assessed age effects on transmissibility and the infectivity of COVID-19 cases during their incubation period. Results: A total of 195 unrelated clusters with 212 primary cases, 137 nonprimary (secondary or tertiary) cases and 1938 uninfected close contacts were traced. We estimated the household SAR to be 13.8% (95% CI: 11.1-17.0%) if household contacts are defined as all close relatives and 19.3% (95% CI: 15.5-23.9%) if household contacts only include those at the same residential address as the cases, assuming a mean incubation period of 4 days and a maximum infectious period of 13 days. The odds of infection among children (<20 years old) was only 0.26 (95% CI: 0.13-0.54) times of that among the elderly ([≥]60 years old). There was no gender difference in the risk of infection. COVID-19 cases were at least as infectious during their incubation period as during their illness. On average, a COVID-19 case infected 0.48 (95% CI: 0.39-0.58) close contacts. Had isolation not been implemented, this number increases to 0.62 (95% CI: 0.51-0.75). The effective reproductive number in Guangzhou dropped from above 1 to below 0.5 in about 1 week. Conclusion: SARS-CoV-2 is more transmissible in households than SARS-CoV and MERS-CoV, and the elderly [≥]60 years old are the most vulnerable to household transmission. Case finding and isolation alone may be inadequate to contain the pandemic and need to be used in conjunction with heightened restriction of human movement as implemented in Guangzhou.