Patterns of COVID-19 pandemic dynamics following deployment of a broad national immunization program

Studies on the real-life impact of the BNT162b2 vaccine, recently authorized for the prevention of coronavirus disease 2019 (COVID-19), are urgently needed. Here, we analysed the temporal dynamics of the number of new COVID-19 cases and hospitalization in Israel following a rapid vaccination campaign initiated on December 20th, 2020. We conducted a retrospective descriptive analysis of data originating from the Israeli Ministry of Health (MOH) from March 2020 to February 2021. In order to distill the possible effect of the vaccinations from other factors, including a third lockdown imposed in Israel on January 2021, we compared the time-dependent changes in number of COVID-19 cases and hospitalizations between (1) individuals aged 60 years and older, eligible to receive the vaccine earlier, and younger age groups; (2) the latest lockdown (which was imposed in parallel to the vaccine rollout) versus the previous lockdown, imposed on September 2020; (3) early-vaccinated cities compared to late-vaccinated cities; and (4) early-vaccinated geographical statistical areas (GSAs) compared to late-vaccinated GSAs. In mid-January, the number of COVID-19 cases and hospitalization started to decline, with a larger and earlier decrease among older individuals, followed by younger age groups, by the order in which they were prioritized for vaccination. This fast and early decline in older individuals was more evident in early-vaccinated compared to late-vaccinated cities. Such a pattern was not observed in the previous lockdown. Our analysis demonstrates evidence for the real-life impact of a national vaccination campaign in Israel on the pandemic dynamics. We believe that our findings have major public health implications in the struggle against the COVID-19 pandemic, including the public s perception of the need for and benefit of nationwide vaccination campaigns. More studies aimed at assessing the effectiveness and impact of vaccination both on the individual and on the population level, with longer followup, are needed.

Socioeconomic Disparities and COVID-19 Vaccination Acceptance: Experience from Israel

COVID-19 vaccination acceptance has a key role in mitigating the pandemic. Concern has been raised that vaccination rates will be limited in demographically defined areas of lower income. Israels rapid vaccination campaign may allow to assess these assumptions in real-world and to devise tools for effectively focusing the vaccination efforts. We analyzed the correlation between COVID-19 vaccination rates, socioeconomic status (SES) and active COVID-19 disease burden. We carried out a nationwide study, based on data provided by Ministry of Health of COVID-19 vaccination rates in all municipalities in Israel up to January 12th, 2021. Municipal Vaccination rates of population older than 60 significantly correlated with the socioeconomic status (r=0.83, 95% confidence interval [0.79 to 0.87]). Finally, we established a novel metric for focusing the vaccination efforts based on % vaccinations and active disease burden. In Israel, a case-model country for COVD-19 vaccinations, vaccination rates were strongly correlated with SES. The study findings demonstrate the need to directly target vaccination acceptance to socio-economically disadvantaged populations and suggest potential tools for policymakers to focus their efforts.

Hospital load and increased COVID-19 related mortality - a nationwide study in Israel

The spread of Coronavirus disease 19 (COVID-19) has led to many healthcare systems being overwhelmed by the rapid emergence of new cases within a short period of time. We explore the ramifications of hospital load due to COVID-19 morbidity on COVID-19 in-hospital patient mortality. We address this question with a nationwide study based on the records of all 22,636 COVID-19 patients hospitalized in Israel from mid-July 2020 to mid-January 2021. We show that even under moderately heavy patient load (>500 countrywide hospitalized severely-ill patients; the Israeli Ministry of Health defined 800 severely-ill patients as the maximum capacity allowing adequate treatment), in-hospital mortality rate of patients with COVID-19 significantly increased compared to periods of lower patient load (250-500 severely-ill patients): 14-day mortality rates were 22.1% (Standard Error 3.1%) higher (mid-September to mid-October) and 27.2% (Standard Error 3.3%) higher (mid-December to mid-January). We further show this higher mortality rate cannot be attributed to changes in the patient population during periods of heavier load.

Predicting illness trajectory and hospital resource utilization of COVID-19 hospitalized patients - a nationwide study

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

Performing risk stratification for COVID-19 when individual level data is not available, the experience of a large healthcare organization

With the global coronavirus disease 2019 (COVID-19) pandemic, there is an urgent need for risk stratification tools to support prevention and treatment decisions. The Centers for Disease Control and Prevention (CDC) listed several criteria that define high-risk individuals, but multivariable prediction models may allow for a more accurate and granular risk evaluation. In the early days of the pandemic, when individual level data required for training prediction models was not available, a large healthcare organization developed a prediction model for supporting its COVID-19 policy using a hybrid strategy. The model was constructed on a baseline predictor to rank patients according to their risk for severe respiratory infection or sepsis (trained using over one-million patient records) and was then post-processed to calibrate the predictions to reported COVID-19 case fatality rates. Since its deployment in mid-March, this predictor was integrated into many decision-processes in the organization that involved allocating limited resources. With the accumulation of enough COVID-19 patients, the predictor was validated for its accuracy in predicting COVID-19 mortality among all COVID-19 cases in the organization (3,176, 3.1% death rate). The predictor was found to have good discrimination, with an area under the receiver-operating characteristics curve of 0.942. Calibration was also good, with a marked improvement compared to the calibration of the baseline model when evaluated for the COVID-19 mortality outcome. While the CDC criteria identify 41% of the population as high-risk with a resulting sensitivity of 97%, a 5% absolute risk cutoff by the model tags only 14% to be at high-risk while still achieving a sensitivity of 90%. To summarize, we found that even in the midst of a pandemic, shrouded in epidemiologic "fog of war" and with no individual level data, it was possible to provide a useful predictor with good discrimination and calibration.

Climate effect on COVID-19 spread rate: an online surveillance tool

BackgroundCOVID-19 outbreak poses an unprecedented challenge for societies, healthcare organizations and economies. In the present analysis we coupled climate data with COVID-19 spread rates worldwide, and in a single country (USA). MethodsData of confirmed COVID-19 cases was derived from the COVID-19 Global Cases by the CSSE at Johns Hopkins University up to March 19, 2020. We assessed disease spread by two measures: replication rate (RR), the slope of the logarithmic curve of confirmed cases, and the rate of spread (RoS), the slope of the linear regression of the logarithmic curve. ResultsBased on predefined criteria, the mean COVID-19 RR was significantly lower in warm climate countries (0.12{+/-}0.02) compared with cold countries (0.24{+/-}0.01), (P<0.0001). Similarly, RoS was significantly lower in warm climate countries 0.12{+/-}0.02 vs. 0.25 {+/-} 0.01 than in cold climate countries (P<0.001). In all countries (independent of climate classification) both RR and RoS displayed a moderate negative correlation with temperature R= -0.69, 95% confidence interval [CI], -0.87 to -0.36; P<0.001 and R= -0.72, 95% confidence interval [CI], -0.87 to -0.36; P<0.001, respectively. We identified a similar moderate negative correlation with the dew point temperature. Additional climate variables did not display a significant correlation with neither RR nor RoS. Finally, in an ancillary analysis, COVID-19 intra-country model using an inter-state analysis of the USA did not identify yet correlation between climate parameters and RR or RoS as of March, 19, 2020. ConclusionsOur analysis suggests a plausible negative correlation between warmer climate and COVID-19 spread rate as defined by RR and RoS worldwide. This initial correlation should be interpreted cautiously and be further validated over time, the pandemic is at different stages in various countries as well as in regions within these countries. As such, some associations may be more affected by local transmission patterns rather than by climate. Importantly, we provide an online surveillance dashboard (https://covid19.net.technion.ac.il/) to further assess the association between climate parameters and outbreak dynamics worldwide as time goes by. Research in contextO_ST_ABSEvidence before this studyC_ST_ABSThe coronavirus, COVID-19 pandemic caused by the novel SARS-CoV 2, challenges healthcare organizations and economies worldwide. There have been previous reports describing the association between seasonal climactic variance and SARS-CoV 1 as well as the MERS infections, but the association with SARS-CoV 2 and climate has not been described extensively. Added value of this studyOur analysis demonstrates a plausible negative correlation between warmer climate and COVID-19 spread rate as defined by RR and RoS worldwide in all countries with local transmission as of March 9, 2020. This initial correlation should be interpreted cautiously and be further validated over time. Importantly, we provide an online surveillance dashboard available at (https://covid19.net.technion.ac.il/) for further dynamic tracking of climate effect on COVID-19 disease spread rate worldwide and on intra-country analysis between USA states. Implications of all the available evidenceOur findings of decreased replication and spread rates of COVID-19 in warm climates may suggest that the inevitable seasonal variance will alter the dynamic of the disease spread in both hemispheres in the coming months. However, we warrant a cautious interpretation of these findings given the fact that we are in the initial steps of this outbreak in many "warm" climate countries, the high variance of the data and the dynamic changes in the disease surveillance and the lack of correlation based on the limited data in the US. We hope that the online tool coupling COVID-19 data with climate data will assist in tracking the disease and tailoring the needed measures to contain it.