{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,5,5]],"date-time":"2025-05-05T04:05:08Z","timestamp":1746417908527,"version":"3.40.4"},"reference-count":27,"publisher":"Wiley","issue":"5","license":[{"start":{"date-parts":[[2025,3,12]],"date-time":"2025-03-12T00:00:00Z","timestamp":1741737600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Tropical Med Int Health"],"published-print":{"date-parts":[[2025,5]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Background<\/jats:title><jats:p>To demonstrate the application and utility of geostatistical modelling to provide comprehensive high\u2010resolution understanding of the population's protective immunity during a pandemic and identify pockets with sub\u2010optimal protection.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>Using data from a national cross\u2010sectional household survey of 6620 individuals in the Dominican Republic (DR) from June to October 2021, we developed and applied geostatistical regression models to estimate and predict Severe Acute Respiratory Syndrome Coronavirus 2 (SARS\u2010CoV\u20102) spike (anti\u2010S) antibodies (Ab) seroprevalence at high resolution (1\u2009km) across heterogeneous areas.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Spatial patterns in population immunity to SARS\u2010CoV\u20102 varied across the DR. In urban areas, a one\u2010unit increase in the number of primary healthcare units per population and 1% increase in the proportion of the population aged under 20\u2009years were associated with higher odds ratios of being anti\u2010S Ab positive of 1.38 (95% confidence interval [CI]: 1.35\u20131.39) and 1.35 (95% CI: 1.32\u20131.33), respectively. In rural areas, higher odds of anti\u2010S Ab positivity, 1.45 (95% CI: 1.39\u20131.51), were observed with increasing temperature in the hottest month (per\u00b0C), and 1.51 (95% CI: 1.43\u20131.60) with increasing precipitation in the wettest month (per mm).<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>A geostatistical model that integrates contextually important socioeconomic and environmental factors can be used to create robust and reliable predictive maps of immune protection during a pandemic at high spatial resolution and will assist in the identification of highly vulnerable areas.<\/jats:p><\/jats:sec>","DOI":"10.1111\/tmi.14094","type":"journal-article","created":{"date-parts":[[2025,3,13]],"date-time":"2025-03-13T02:30:32Z","timestamp":1741833032000},"page":"382-392","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Spatial prediction of immunity gaps during a pandemic to 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