ABSTRACT
Worldwide shortage of vaccination against SARS-CoV-2 infection while the pandemic is still uncontrolled leads many states to the dilemma whether or not to vaccinate previously infected persons. Understanding the level of protection of previous infection compared to that of vaccination is critical for policy making. We analyze an updated individual-level database of the entire population of Israel to assess the protection efficacy of both prior infection and vaccination in preventing subsequent SARS-CoV-2 infection, hospitalization with COVID-19, severe disease, and death due to COVID-19. Vaccination was highly effective with overall estimated efficacy for documented infection of 92{middle dot}8% (CI:[92{middle dot}6, 93{middle dot}0]); hospitalization 94{middle dot}2% (CI:[93{middle dot}6, 94{middle dot}7]); severe illness 94{middle dot}4% (CI:[93{middle dot}6, 95{middle dot}0]); and death 93{middle dot}7% (CI:[92{middle dot}5, 94{middle dot}7]). Similarly, the overall estimated level of protection from prior SARS-CoV-2 infection for documented infection is 94{middle dot}8% (CI:[94{middle dot}4, 95{middle dot}1]); hospitalization 94{middle dot}1% (CI:[91{middle dot}9, 95{middle dot}7]); and severe illness 96{middle dot}4% (CI:[92{middle dot}5, 98{middle dot}3]). Our results question the need to vaccinate previously-infected individuals.
ABSTRACT
BackgroundThe spread of COVID-19 has led to a severe strain on hospital capacity in many countries. There is a need for a model to help planners assess expected COVID-19 hospital resource utilization. MethodsRetrospective nationwide cohort study following the day-by-day clinical status of all hospitalized COVID-19 patients in Israel from March 1st to May 2nd, 2020. Patient clinical course was modelled with a machine learning approach based on a set of multistate Cox regression-based models with adjustments for right censoring, recurrent events, competing events, left truncation, and time-dependent covariates. The model predicts the patients entire disease course in terms of clinical states, from which we derive the patients hospital length-of-stay, length-of-stay in critical state, the risk of in-hospital mortality, and total and critical care hospital-bed utilization. Accuracy assessed over eight cross-validation cohorts of size 330, using per-day Mean Absolute Error (MAE) of predicted hospital utilization averaged over 64 days; and area under the receiver operating characteristics (AUROC) for individual risk of critical illness and in-hospital mortality, assessed on the first day of hospitalization. We present predicted hospital utilization under hypothetical incoming patient scenarios. FindingsDuring the study period, 2,703 confirmed COVID-19 patients were hospitalized in Israel. The per-day MAEs for total and critical-care hospital-bed utilization, were 4{middle dot}72 {+/-} 1{middle dot}07 and 1{middle dot}68 {+/-} 0{middle dot}40 respectively; the AUROCs for prediction of the probabilities of critical illness and in-hospital mortality were 0{middle dot}88 {+/-} 0{middle dot}04 and 0{middle dot}96 {+/-} 0{middle dot}04, respectively. We further present the impact of several scenarios of patient influx on healthcare system utilization, and provide an R software package for predicting hospital-bed utilization. InterpretationWe developed a model that, given basic easily obtained data as input, accurately predicts total and critical care hospital utilization. The model enables evaluating the impact of various patient influx scenarios on hospital utilization and planning ahead of hospital resource allocation. FundingThe work was funded by the Israeli Ministry of Health. M.G. received support from the U.S.-Israel Binational Science Foundation (BSF, 2016126). O_TEXTBOXResearch in contextO_ST_ABSEvidence before this studyC_ST_ABSCOVID19 outbreaks are known to lead to severe case load in hospital systems, stretching resources, partially due to the long hospitalizations needed for some of the patients. There is a crucial need for tools helping planners assess future hospitalization load, taking into account the specific characteristics and heterogeneity of currently hospitalized COVID19 patients, as well as the characteristics of incoming patients. We searched PubMed for articles published up to September 9, 2020, containing the words "COVID19" and combinations of "hospital", "utilization", "resource", "capacity" and "predict". We found 145 studies; out of them, several included models that predict the future trend of hospitalizations using compartment models (e.g. SIR models), or by using exponential or logistic models. We discuss two of the more prominent ones, which model explicitly the passage of patients through the ICU. These models (i) do not take into account individual patient characteristics; (ii) do not consider length-of-stay heterogeneity, despite the fact that bed utilization is in part determined by a long tail of patients requiring significantly longer stays than others; (iii) do not correct for competing risks bias. We further searched for studies containing the words "COVID19" and "multistate", and "COVID19" and "length" and "stay". Out of 317 papers, we found two using multistate models focusing only on patients undergoing ECMO treatment. Added value of this studyWe present the first model predicting hospital load based on the individual characteristics of hospitalized patients: age, sex, clinical state, and time already spent in-hospital. We combine this with scenarios for incoming patients, allowing for variations by age, sex and clinical state. The models precise predictions are based on a large sample of complete, day-by-day disease trajectories of patients, with a full coverage of the entire COVID-19 hospitalized population in Israel up to early May, 2020 (n =2, 703). We provide the model, as well as software for fitting such a model to local data, and an anonymized version of the dataset used to create the model. Implications of all the available evidenceAccurate predictions for hospital utilization can be made based on easy to obtain patient data: age, sex, and patient clinical state (moderate, severe or critical). The model allows hospital-and regional-level planners to allocate resources in a timely manner, preparing for different patient influx scenarios. C_TEXTBOX
ABSTRACT
BackgroundOne of the significant unanswered questions about COVID-19 epidemiology relates to the role of children in transmission. In this study we estimate susceptibility and infectivity of children compared to those of adults. Understanding the age-structured transmission dynamics of the outbreak provides precious and timely information to guide epidemic modelling and public health policy. MethodsData were collected from households in the city of Bnei Brak, Israel, in which all household members were tested for COVID-19 using PCR. To estimate relative transmission parameters in the absence of data on who infected whom, we developed an estimation method based on a discrete stochastic dynamic model of the spread of the epidemic within a household. The model describes the propagation of the disease between household members allowing for susceptibility and infectivity parameters to vary among two age groups. The parameter estimates are obtained by a maximum likelihood method, where the likelihood function is computed based on the stochastic model via simulations. FindingsInspection of the data reveals that children are less likely to become infected compared to adults (25% of children infected over all households, 44% of adults infected over all households, excluding index cases), and the chances of becoming infected increases with age. An interesting exception is that infants up to age one year are more likely to be infected than children between one and four. Using our modelling approach, we estimate that the susceptibility of children (under 20 years old) is 45% [40%, 55%] of the susceptibility of adults. The infectivity of children was estimated to be 85% [65%, 110%] relative to that of adults. InterpretationIt is widely observed that the percentage of children within confirmed cases is low. A common explanation is that children who are infected are less likely to develop symptoms than adults, and thus are less likely to be tested. We estimate that children are less susceptible to infection, which is an additional factor explaining their relatively low rate of occurrence within confirmed cases. Moreover, our results indicate that children, when infected, are somewhat less prone to infect others compared to adults; however, this result is not statistically significant. The resulting estimates of susceptibility and infectivity of children compared to adults are crucial for deciding on appropriate interventions, and for controlling the epidemic outbreak and its progress. These estimates can guide age-dependent public health policy such as school closure and opening. However, while our estimates of childrens susceptibility and infectivity are lower than those of adults within a household, it is important to bear in mind that their role in the spread of COVID-19 outside the household, is also affected by different contact patterns and hygiene habits.
