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A spatial model to jointly analyze self-reported survey data of COVID-19 symptoms and official COVID-19 incidence data.

ABSTRACT: This work presents a joint spatial modeling framework to improve estimation of the spatial distribution of the latent COVID-19 incidence in Belgium, based on test-confirmed COVID-19 cases and crowd-sourced symptoms data as reported in a large-scale online survey. Correction is envisioned for stochastic dependence between the survey's response rate and spatial COVID-19 incidence, commonly known as preferential sampling, but not found significant. Results show that an online survey can provide valuable auxiliary data to optimize spatial COVID-19 incidence estimation based on confirmed cases in situations with limited testing capacity. Furthermore, it is shown that an online survey on COVID-19 symptoms with a sufficiently large sample size per spatial entity is capable of pinpointing the same locations that appear as test-confirmed clusters, approximately 1 week earlier. We conclude that a large-scale online study provides an inexpensive and flexible method to collect timely information of an epidemic during its early phase, which can be used by policy makers in an early phase of an epidemic and in conjunction with other monitoring systems.

SUBMITTER: Vranckx M

PROVIDER: S-EPMC9349774 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature

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A spatial model to jointly analyze self-reported survey data of COVID-19 symptoms and official COVID-19 incidence data.

Vranckx Maren M Faes Christel C Molenberghs Geert G Hens Niel N Beutels Philippe P Van Damme Pierre P Aerts Jan J Petrof Oana O Pepermans Koen K Neyens Thomas T

Biometrical journal. Biometrische Zeitschrift 20220711 1

This work presents a joint spatial modeling framework to improve estimation of the spatial distribution of the latent COVID-19 incidence in Belgium, based on test-confirmed COVID-19 cases and crowd-sourced symptoms data as reported in a large-scale online survey. Correction is envisioned for stochastic dependence between the survey's response rate and spatial COVID-19 incidence, commonly known as preferential sampling, but not found significant. Results show that an online survey can provide val ...[more]

PMID: 35818698

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Project description:When studying the dynamics of morbidity and mortality, one should not limit ourselves to analyzing general trends. Interesting information can be obtained from the analysis of deviations in morbidity and mortality from the general dynamics. Comparison of the cases of morbidity or death for adjacent time intervals allows us to find out whether the changes in conditions were for short periods of time and whether the cases of morbidity or death were independent. The article consists of two parts: Study of the probability distribution (CDF) of the difference between two independent observations of the Poisson distribution; Application of the results to analyze the morbidity and mortality trends by day for the new coronavirus infection. For the distribution function of the module of difference between two independent observations of the Poisson distribution, an analytical expression has been obtained that allows to get an exact solution. A program has been created, whose software can be downloaded at http://1mgmu.com/nau/DeltaPoisson/DeltaPoisson.zip. An approximate solution that does not require complex calculations has also been obtained, which can be used for an average of more than 20. If real difference is greater than expected, it may be in the following cases: morbidity or mortality varies considerably during the day. That could happen, for example, if the registered number of morbidity on Saturday and Sunday is less than on weekdays due to the management model of the health system, or if the cases are not independent; for example, due to the active identification of infected people among those who have come into contact with the patient. If the difference is less than expected, it may be due to external limiting factors, such as a shortage of test systems for making a diagnosis, a limited number of pathologists to determine the cause of death, and so on. In the analysis of the actual data for COVID-19 it was found that for Poland and Russia, excluding Moscow, the difference in the number of cases and deaths is greater than expected, while for Moscow-less than expected. This may be due to the information policy-the effort to somehow reassure Moscow's population, which in the spring of 2020 had a high incidence rate of the new coronavirus infection.

Project description:BackgroundIn a global pandemic, digital technology offers innovative methods to disseminate public health messages. As an example, the messenger app WhatsApp was adopted by both the World Health Organization and government agencies to provide updates on the coronavirus disease (COVID-19). During a time when rumors and excessive news threaten psychological well-being, these services allow for rapid transmission of information and may boost resilience.ObjectiveIn this study, we sought to accomplish the following: (1) assess well-being during the pandemic; (2) replicate prior findings linking exposure to COVID-19 news with psychological distress; and (3) examine whether subscription to an official WhatsApp channel can mitigate this risk.MethodsAcross 8 weeks of the COVID-19 outbreak (March 7 to April 21, 2020), we conducted a survey of 1145 adults in Singapore. As the primary outcome measure, participants completed the Depression, Anxiety, and Stress Scale (DASS-21). As predictor variables, participants also answered questions pertaining to the following: (1) their exposure to COVID-19 news; (2) their use of the Singapore government's WhatsApp channel; and (3) their demographics.ResultsWithin the sample, 7.9% of participants had severe or extremely severe symptoms on at least one DASS-21 subscale. Depression scores were associated with increased time spent receiving COVID-19 updates, whereas use of the official WhatsApp channel emerged as a protective factor (b=-0.07, t[863]=-2.04, P=.04). Similarly, increased anxiety scores were associated with increased exposure to both updates and rumors, but this risk was mitigated by trust in the government's WhatsApp messages (b=-0.05, t[863]=-2.13, P=.03). Finally, although stress symptoms increased with the amount of time spent receiving updates, these symptoms were not significantly related to WhatsApp use.ConclusionsOur findings suggest that messenger apps may be an effective medium for disseminating pandemic-related information, allowing official agencies to reach a broad sector of the population rapidly. In turn, this use may promote public well-being amid an "infodemic."Trial registrationClinicalTrials.gov NCT04305574; https://clinicaltrials.gov/ct2/show/NCT04305574.

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Project description:BackgroundThe COVID-19 pandemic has revealed significant challenges in disease forecasting and in developing a public health response, emphasizing the need to manage missing data from various sources in making accurate forecasts.ObjectiveWe aimed to show how handling missing data can affect estimates of the COVID-19 incidence rate (CIR) in different pandemic situations.MethodsThis study used data from the COVID-19/SARS-CoV-2 surveillance system at the National Institute of Hygiene and Epidemiology, Vietnam. We separated the available data set into 3 distinct periods: zero COVID-19, transition, and new normal. We randomly removed 5% to 30% of data that were missing completely at random, with a break of 5% at each time point in the variable daily caseload of COVID-19. We selected 7 analytical methods to assess the effects of handling missing data and calculated statistical and epidemiological indices to measure the effectiveness of each method.ResultsOur study examined missing data imputation performance across 3 study time periods: zero COVID-19 (n=3149), transition (n=1290), and new normal (n=9288). Imputation analyses showed that K-nearest neighbor (KNN) had the lowest mean absolute percentage change (APC) in CIR across the range (5% to 30%) of missing data. For instance, with 15% missing data, KNN resulted in 10.6%, 10.6%, and 9.7% average bias across the zero COVID-19, transition, and new normal periods, compared to 39.9%, 51.9%, and 289.7% with the maximum likelihood method. The autoregressive integrated moving average model showed the greatest mean APC in the mean number of confirmed cases of COVID-19 during each COVID-19 containment cycle (CCC) when we imputed the missing data in the zero COVID-19 period, rising from 226.3% at the 5% missing level to 6955.7% at the 30% missing level. Imputing missing data with median imputation methods had the lowest bias in the average number of confirmed cases in each CCC at all levels of missing data. In detail, in the 20% missing scenario, while median imputation had an average bias of 16.3% for confirmed cases in each CCC, which was lower than the KNN figure, maximum likelihood imputation showed a bias on average of 92.4% for confirmed cases in each CCC, which was the highest figure. During the new normal period in the 25% and 30% missing data scenarios, KNN imputation had average biases for CIR and confirmed cases in each CCC ranging from 21% to 32% for both, while maximum likelihood and moving average imputation showed biases on average above 250% for both CIR and confirmed cases in each CCC.ConclusionsOur study emphasizes the importance of understanding that the specific imputation method used by investigators should be tailored to the specific epidemiological context and data collection environment to ensure reliable estimates of the CIR.

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A spatial model to jointly analyze self-reported survey data of COVID-19 symptoms and official COVID-19 incidence data.

Publications

A spatial model to jointly analyze self-reported survey data of COVID-19 symptoms and official COVID-19 incidence data.

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