A spatial model to jointly analyze self‐reported survey data of COVID‐19 symptoms and official COVID‐19 incidence data
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 stochas...
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| Veröffentlicht in: | Biometrical journal 2023-01, Vol.65 (1), p.e2100186-n/a |
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| container_title | Biometrical journal |
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| creator | Vranckx, Maren Faes, Christel Molenberghs, Geert Hens, Niel Beutels, Philippe Van Damme, Pierre Aerts, Jan Petrof, Oana Pepermans, Koen Neyens, Thomas |
| description | 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. |
| doi_str_mv | 10.1002/bimj.202100186 |
| format | Article |
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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. 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KGaA</publisher><subject>bivariate conditional autoregressive random effect ; COVID-19 ; COVID-19 - epidemiology ; disease mapping ; Epidemics ; Humans ; Incidence ; Polls & surveys ; preferential sampling ; SARS-CoV-2 ; Self Report ; Spatial analysis ; Spatial data ; Spatial distribution ; Stochasticity ; survey data ; Surveys</subject><ispartof>Biometrical journal, 2023-01, Vol.65 (1), p.e2100186-n/a</ispartof><rights>2022 Wiley‐VCH GmbH.</rights><rights>2022 Wiley-VCH GmbH.</rights><rights>2023 Wiley‐VCH GmbH.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c4634-cf5cd67799961a79b683c357beac29323426a477b8dc23b5ed51349a73c4bee83</citedby><cites>FETCH-LOGICAL-c4634-cf5cd67799961a79b683c357beac29323426a477b8dc23b5ed51349a73c4bee83</cites><orcidid>0000-0002-6509-7777 ; 0000-0001-7294-9491 ; 0000-0002-1802-9640 ; 0000-0003-2364-7555</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://onlinelibrary.wiley.com/doi/pdf/10.1002%2Fbimj.202100186$$EPDF$$P50$$Gwiley$$H</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1002%2Fbimj.202100186$$EHTML$$P50$$Gwiley$$H</linktohtml><link.rule.ids>230,314,780,784,885,1417,27924,27925,45574,45575</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/35818698$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Vranckx, Maren</creatorcontrib><creatorcontrib>Faes, Christel</creatorcontrib><creatorcontrib>Molenberghs, Geert</creatorcontrib><creatorcontrib>Hens, Niel</creatorcontrib><creatorcontrib>Beutels, Philippe</creatorcontrib><creatorcontrib>Van Damme, Pierre</creatorcontrib><creatorcontrib>Aerts, Jan</creatorcontrib><creatorcontrib>Petrof, Oana</creatorcontrib><creatorcontrib>Pepermans, Koen</creatorcontrib><creatorcontrib>Neyens, Thomas</creatorcontrib><title>A spatial model to jointly analyze self‐reported survey data of COVID‐19 symptoms and official COVID‐19 incidence data</title><title>Biometrical journal</title><addtitle>Biom J</addtitle><description>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. 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| source | MEDLINE; Access via Wiley Online Library |
| subjects | bivariate conditional autoregressive random effect COVID-19 COVID-19 - epidemiology disease mapping Epidemics Humans Incidence Polls & surveys preferential sampling SARS-CoV-2 Self Report Spatial analysis Spatial data Spatial distribution Stochasticity survey data Surveys |
| title | A spatial model to jointly analyze self‐reported survey data of COVID‐19 symptoms and official COVID‐19 incidence data |
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