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.

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.

Anosmia and other SARS-CoV-2 positive test-associated symptoms, across three national, digital surveillance platforms as the COVID-19 pandemic and response unfolded: an observation study

BackgroundMultiple participatory surveillance platforms were developed across the world in response to the COVID-19 pandemic, providing a real-time understanding of community-wide COVID-19 epidemiology. During this time, testing criteria broadened and healthcare policies matured. We sought to test whether there were consistent associations of symptoms with SARS-CoV-2 test status across three national surveillance platforms, during periods of testing and policy changes, and whether inconsistencies could better inform our understanding and future studies as the COVID-19 pandemic progresses. MethodsFour months (1st April 2020 to 31st July 2020) of observation through three volunteer COVID-19 digital surveillance platforms targeting communities in three countries (Israel, United Kingdom, and United States). Logistic regression of self-reported symptom on self-reported SARS-CoV-2 test status (or test access), adjusted for age and sex, in each of the study cohorts. Odds ratios over time were compared to known changes in testing policies and fluctuations in COVID-19 incidence. FindingsAnosmia/ageusia was the strongest, most consistent symptom associated with a positive COVID-19 test, based on 658,325 tests (5% positive) from over 10 million respondents in three digital surveillance platforms using longitudinal and cross-sectional survey methodologies. During higher-incidence periods with broader testing criteria, core COVID-19 symptoms were more strongly associated with test status. Lower incidence periods had, overall, larger confidence intervals. InterpretationThe strong association of anosmia/ageusia with self-reported SARS-CoV-2 test positivity is omnipresent, supporting its validity as a reliable COVID-19 signal, regardless of the participatory surveillance platform or testing policy. This analysis highlights that precise effect estimates, as well as an understanding of test access patterns to interpret differences, are best done only when incidence is high. These findings strongly support the need for testing access to be as open as possible both for real-time epidemiologic investigation and public health utility. FundingNIH, NIHR, Alzheimers Society, Wellcome Trust Research in contextO_ST_ABSEvidence before this studyC_ST_ABSAs the COVID-19 pandemic has evolved, testing capacity expanded and governmental guidelines adapted, generally encouraging testing with a broader set of symptoms, not just fever with respiratory symptoms. In parallel, multiple large-scale citizen science digital surveillance platforms launched to complement knowledge from laboratory and somewhat smaller clinical studies. Symptoms such as loss of sense of smell have been identified as strongly predictive of COVID-19 infection in both clinical and syndromic surveillance analyses, and have therefore been used to inform these testing policy changes and access expansion. Added value of this studyThis study identifies symptoms that are or are not consistently associated with SARS-CoV-2 test positivity over time and across three country-based COVID-19 surveillance platforms in the United States, United Kingdom and Israel. These platforms are website and smartphone based, as well as cross-sectional and longitudinal. The study period of 4 months covers fluctuating COVID-19 prevalence during the fall of the first wave and, in some areas, rise of the second wave. In addition, the study period overlaps expansion of test access and test seeking. Importantly, these analyses track and highlight the value of individual symptoms to predict SARS-CoV-2 test positivity under a range of conditions. Implications of all the available evidenceDespite differences in surveillance methodology, access to SARS-CoV-2 testing and disease prevalence, loss of sense of smell or taste was consistently the strongest predictor of COVID-19 infection across all platforms over time. As access to testing broadened, the relevance of COVID-like symptoms and consistency of their predictive ability became apparent. However, confidence bounds generally widened with a fall in COVID-19 incidence. Therefore, for the most robust symptom-based COVID-19 prediction models should consider surveillance data during periods of higher incidence and improved test access, and effect estimates that replicate across different epidemiologic conditions and platforms.

Stress-related emotional and behavioural impact following the first COVID-19 outbreak peak

BackgroundThe coronavirus disease 2019 (COVID-19) pandemic poses multiple psychologically-stressful challenges and is associated with increased risk for mental illness. Previous studies have mostly focused on the psychopathological symptoms associated with the outbreak peak. MethodsWe examined the behavioural and mental health impact of the pandemic in Israel using an online survey. We collected 12,125 responses from 4,933 adult respondents during six weeks encompassing the end of the first outbreak and the beginning of the second. We used clinically validated instruments (Brief Symptom Inventory 18 (BSI-18), Perceived stress scale (PSS), Brief COPE inventory) to assess anxiety- and depression-related emotional distress, symptoms, and coping strategies, as well as questions designed to specifically assess COVID-19-related concerns. ResultsRespondents indicated worrying more about the situation in their country and their close ones contracting the virus, than about their own health and financial situation. The reported distress correlates with the number of new COVID-19 cases and higher emotional burden was associated with being female, younger, unemployed, living in low socioeconomic status localities, encountering more people, and experiencing physiological symptoms. Unexpectedly, older age and having a prior medical condition were associated with reduced emotional distress. ConclusionsOur findings show that inequalities in mental-health burden associated with the COVID-19 pandemic are relevant also following the initial outbreak, and highlight the environmental context and its importance in understanding individual ability to cope with the long-term stressful challenges of the pandemic.

Longitudinal symptom dynamics of COVID-19 infection in primary care

ObjectiveData regarding the clinical characteristics of COVID-19 infection is rapidly accumulating. However, most studies thus far are based on hospitalized patients and lack longitudinal follow up. As the majority of COVID-19 cases are not hospitalized, prospective studies of symptoms in the population presenting to primary care are needed. Here, we assess the longitudinal dynamic of clinical symptoms in non-hospitalized individuals prior to and throughout the diagnosis of SARS-CoV-2 infection. DesignData on symptoms were extracted from electronic health records (EHR) consisting of both results of PCR tests and symptoms recorded by primary care physicians, and linked longitudinal self reported symptoms. SettingThe second largest Health Maintenance Organization in Israel, Maccabi Health Services ParticipantsFrom 1/3/2020 to 07/06/2020, information on symptoms from either surveys or primary care visits was available for 206,377 individuals, including 2,471 who tested positive for COVID-19. Main OutcomesLongitudinal prevalence of clinical symptoms in COVID-19 infection diagnosed by PCR testing for SARS-CoV-2 from nasopharyngeal swabs. ResultsIn adults, the most prevalent symptoms recorded in EHR were cough (11.6%), fever (10.3%), and myalgia (7.7%) and the most prevalent self-reported symptoms were cough (21%), fatigue (19%) and rhinorrhea and/or nasal congestion (17%). In children, the most prevalent symptoms recorded in the EHR were fever (7%), cough (5.5%) and abdominal pain (2.4%). Emotional disturbances were documented in 15.9% of the positive adults and 4.2% of the children. Loss of taste and smell, either self-reported or documented by a physician, 3 weeks prior to testing, were the most discriminative symptoms in adults (OR =11.18 and OR=5.47 respectively). Additional symptoms included self reported headache (OR = 2.03) and fatigue (OR = 1.73) and a documentation of syncope, rhinorrhea (OR = 2.09 for both) and fever (OR= 1.62) by a physician. Mean time to recovery was 23.5 {+/-} 9.9 days. Children had a significantly shorter disease duration (21.7 {+/-} 8.8 days, p-value=0.01). Several symptoms, including fatigue, myalgia, runny nose and shortness of breath were reported weeks after recovery. ConclusionsAs the COVID-19 pandemic progresses rapidly worldwide, obtaining accurate information on symptoms and their progression is of essence. Our study shed light on the full clinical spectrum of symptoms experienced by infected individuals in primary care, and may alert physicians for the possibility of COVID-19 infection.

Who should we test for COVID-19?A triage model built from national symptom surveys

The gold standard for COVID-19 diagnosis is detection of viral RNA in a reverse transcription PCR test. Due to global limitations in testing capacity, effective prioritization of individuals for testing is essential. Here, we devised a model that estimates the probability of an individual to test positive for COVID-19 based on answers to 9 simple questions regarding age, gender, presence of prior medical conditions, general feeling, and the symptoms fever, cough, shortness of breath, sore throat and loss of taste or smell, all of which have been associated with COVID-19 infection. Our model was devised from a subsample of a national symptom survey that was answered over 2 million times in Israel over the past 2 months and a targeted survey distributed to all residents of several cities in Israel. Overall, 43,752 adults were included, from which 498 self-reported as being COVID-19 positive. We successfully validated the model on held-out individuals from Israel where it achieved a positive predictive value (PPV) of 46.3% at a 10% sensitivity and demonstrated its applicability outside of Israel by further validating it on an independently collected symptom survey dataset from the U.K., U.S. and Sweden, where it achieved a PPV of 34.7% at 10% sensitivity. Moreover, evaluating the models performance on this latter independent dataset on entries collected one week prior to the PCR test and up to the day of the test we found the highest performance on the day of the test. As our tool can be used online and without the need of exposure to suspected patients, it may have worldwide utility in combating COVID-19 by better directing the limited testing resources through prioritization of individuals for testing, thereby increasing the rate at which positive individuals can be identified and isolated.

The effect of a national lockdown in response to COVID-19 pandemic on the prevalence of clinical symptoms in the population

The vast and rapid spread of COVID-19 calls for immediate action from policy-makers, and indeed, many countries have implemented lockdown measures to varying degrees. Here, we utilized nationwide surveys that assess COVID-19 associated symptoms to analyse the effect of the lockdown policy in Israel on the prevalence of clinical symptoms in the population. Daily symptom surveys were distributed online and included questions regarding fever, respiratory symptoms, gastrointestinal symptoms, anosmia and ageusia. A total of 2,071,349 survey responses were analysed. We defined a single measure of symptoms, Symptoms Average (SA), as the mean number of symptoms reported by responders. Data were collected between March 15th to June 3rd, 2020. Notably, on the population level, following severe lockdown measures between March 15 th and April 20th, SA sharply declined by 83.8% (p < 0.05), as did every single symptom, including the most common symptoms reported by our responders, cough and rhinorrhea and\or nasal congestion, which decreased by 74.1% (p < 0.05) and 69.6% (p < 0.05), respectively. Similarly, on the individual level, analysis of repeated responses from the same individuals (N = 208,637) over time also showed a decrease in symptoms during this time period. Moreover, the reduction in symptoms was observed in all cities in Israel, and in several stratifications of demographic characteristics. Different symptoms exhibit different reduction dynamics, suggesting differences in the nature of the symptoms or in the underlying medical conditions. Between May 13th and June 3rd, following several subsequent lockdown relief measures, we observed an increase in individual symptoms and in SA, which increased by 31.42%. Overall, these results demonstrate a profound decrease in a variety of clinical symptoms following the implementation of a lockdown in Israel, and an increase in the prevalence of symptoms following the loosening of lockdown restrictions. As our survey symptoms are not specific to COVID-19 infection, this effect likely represents an overall nationwide reduction in the prevalence of infectious diseases, including COVID-19. This quantification may be of major interest for COVID-19 pandemic, as many countries consider implementation of lockdown strategies.

Comparison of SARS-CoV-2 Exit Strategies Building Blocks

We consider and compare various exit strategy building blocks and key measures to mitigate the current SARS-CoV-2 pandemic, some already proposed as well as improvements we suggest. Our comparison is based on a computerized simulation integrating accumulated SARS-CoV-2 epidemiological knowledge. Our results stress the importance of immediate on-symptom isolation of suspected cases and household members, and the beneficial effects of prompt testing capacity. Our findings expose significant epidemic-suppression differences among strategies with seemingly similar economic cost stressing the importance of not just the portion of population and business that is released, but also the pattern. The most effective building blocks are the ones that integrate several base strategies - they allow to release large portions of the population while still achieving diminishing viral spread. However, it may come with a price on somewhat more complex schemes. For example, our simulations indicate that a personal isolation of 4 days once every two weeks, for example a long weekend (Fri-Mon) self-isolation once every two weeks, while protecting the 5% most sensitive population would reduce R well below 1 even if ten percent of the population do not follow it. This kind of integrated strategy can be either voluntary or mandatory and enforced. We further simulate the contrasting approach of a stratified population release in a hope to achieve herd immunity, which for the time being seems inferior to other suggested building blocks. Knowing the tradeoff between building blocks could help optimize exit strategies to be more effective and suitable for a particular area or country, while maximizing human life as well as economic value. Given our results, we believe that pandemic can be controlled within a reasonable amount of time and at a reasonable socio-economic burden.

Building an International Consortium for Tracking Coronavirus Health Status

Information is the most potent protective weapon we have to combat a pandemic, at both the individual and global level. For individuals, information can help us make personal decisions and provide a sense of security. For the global community, information can inform policy decisions and offer critical insights into the epidemic of COVID-19 disease. Fully leveraging the power of information, however, requires large amounts of data and access to it. To achieve this, we are making steps to form an international consortium, Coronavirus Census Collective (CCC, coronaviruscensuscollective.org), that will serve as a hub for integrating information from multiple data sources that can be utilized to understand, monitor, predict, and combat global pandemics. These sources may include self-reported health status through surveys (including mobile apps), results of diagnostic laboratory tests, and other static and real-time geospatial data. This collective effort to track and share information will be invaluable in predicting hotspots of disease outbreak, identifying which factors control the rate of spreading, informing immediate policy decisions, evaluating the effectiveness of measures taken by health organizations on pandemic control, and providing critical insight on the etiology of COVID-19. It will also help individuals stay informed on this rapidly evolving situation and contribute to other global efforts to slow the spread of disease. In the past few weeks, several initiatives across the globe have surfaced to use daily self-reported symptoms as a means to track disease spread, predict outbreak locations, guide population measures and help in the allocation of healthcare resources. The aim of this paper is to put out a call to standardize these efforts and spark a collaborative effort to maximize the global gain while protecting participant privacy.

A framework for identifying regional outbreak and spread of COVID-19 from one-minute population-wide surveys

Coronavirus infection spreads in clusters and therefore early identification of these clusters is critical for slowing down the spread of the virus. Here, we propose that daily population-wide surveys that assess the development of symptoms caused by the virus could serve as a strategic and valuable tool for identifying such clusters to inform epidemiologists, public health officials, and policy makers. We show preliminary results from a survey of over 58,000 Israelis and call for an international consortium to extend this concept in order to develop predictive models. We expect such data to allow: Faster detection of spreading zones and patients; Obtaining a current snapshot of the number of people in each area who have developed symptoms; Predicting future spreading zones several days before an outbreak occurs; Evaluating the effectiveness of the various social distancing measures taken, and their contribution to reduce the number of symptomatic people. Such information can provide a valuable tool for decision makers to decide which areas need strengthening of social distancing measures and which areas can be relieved. Preliminary analysis shows that in neighborhoods with confirmed COVID-19 patient history, more responders report on COVID-19 associated symptoms, demonstrating the potential utility of our approach for detection of outbreaks. Researchers from other countries including the U.S, India, Italy, Spain, Germany, Mexico, Finland, Sweden, Norway and several others have adopted our approach and we are collaborating to further improve it. We call with urgency for other countries to join this international consortium, and to share methods and data collected from these daily, simple, one-minute surveys.