ABSTRACT
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.
ABSTRACT
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.
ABSTRACT
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.
ABSTRACT
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.
