Zika virus (ZIKV), classified as a priority pathogen by the World Health Organization, is an Aedes-borne arbovirus that can cause neurological complications and birth defects in newborns of mothers infected during pregnancy. We conducted a systematic review of peer-reviewed studies reporting ZIKV epidemiological parameters, transmission models and outbreaks (PROSPERO CRD42023393345) to characterize its transmissibility, seroprevalence, risk factors, disease sequelae and natural history. We performed meta-analyses of the proportions of congenital Zika syndrome, pregnancy loss among ZIKV-infected mothers and symptomatic cases. We extracted information from 574 studies. Across 418 included studies assigned a high-quality score, we extracted 969 parameters, 127 outbreak records and 154 models. Using random-effects models, we estimated proportions of congenital Zika syndrome (4.65%, 95% confidence interval (CI): 3.38-6.67%), pregnancy loss (2.48%, 95% CI: 1.62-3.78%) and symptomatic cases (51.20%, 95% CI: 38.00-64.23%). Seroprevalence estimates (n = 354) were retrieved beyond South America and French Polynesia. Basic reproduction number estimates (n = 77) ranged between 1.12 and 7.4. We found 66 human epidemiological delay estimates, including the intrinsic incubation period (n = 11, range: 4-12.1 days), infectious period (n = 15, range: 3-50 days), extrinsic incubation period (n = 22, range: 5.1-24.2 days) and serial interval (n = 27, range: 7.4-32.9 days). These data are available in the R package 'epireview' (version 1.4.5). We provide a comprehensive systematic summary of ZIKV epidemiology, revealing large heterogeneities and inconsistencies in the reporting of parameter estimates, study designs and parameter definitions and underscoring the need for standardized epidemiological definitions.

Abstract Cancer screening can enable early detection and improve survival but a focus on single cancers limits cost-effectiveness. Here we present OMAFound (carcinOMA Finder foundation), a foundation model capable of simultaneous multi-cancer screening at both organ level and patient level using widely accessible non-contrast computed tomography (CT). The model was developed and tested on 325,197 CT volumes from 151,386 patients across 10 Chinese and international datasets, achieving performance comparable to mammography-based approaches for breast cancer detection and matching existing lung-specific models for lung cancer detection. In a prospective multi-centre cohort of 21,601 patients undergoing low-dose CT screening, OMAFound demonstrated balanced accuracy of 82.2% for breast cancer and 88.0% for lung cancer in females, while attaining 86.1% balanced accuracy for lung cancer detection in males. When assisted by OMAFound, 7 generalist radiologists showed improvement in sensitivity (mean increases of 38.9% for breast cancer, 16.0% for lung cancer and 21.3% at patient level), without compromising specificity. These findings highlight the potential of OMAFound as a multi-cancer screening tool to offer robust preventive medicine strategies with minimal costs.