Data curation during a pandemic and lessons learned from COVID-19

Detailed, accurate data related to a disease outbreak enable informed public health decision making. Given the variety of data types available across different regions, global data curation and standardization efforts are essential to guarantee rapid data integration and dissemination in times of a pandemic.Data availability The underlying dataset for Fig. 1a is available open access from the supplemental material in ref. 5, and datasets for Fig. 1b,c from the UNESCO World Heritage List 2021 in ref. 32.https://www.nature.com/natcomputscihj2023Computer Scienc

Spatiotemporal incidence of Zika and associated environmental drivers for the 2015-2016 epidemic in Colombia

Despite a long history of mosquito-borne virus epidemics in the Americas, the impact of the Zika virus (ZIKV) epidemic of 2015–2016 was unexpected. The need for scientifically informed decision-making is driving research to understand the emergence and spread of ZIKV. To support that research, we assembled a data set of key covariates for modeling ZIKV transmission dynamics in Colombia, where ZIKV transmission was widespread and the government made incidence data publically available. On a weekly basis between January 1, 2014 and October 1, 2016 at three administrative levels, we collated spatiotemporal Zika incidence data, nine environmental variables, and demographic data into a single downloadable database. These new datasets and those we identified, processed, and assembled at comparable spatial and temporal resolutions will save future researchers considerable time and effort in performing these data processing steps, enabling them to focus instead on extracting epidemiological insights from this important data set. Similar approaches could prove useful for filling data gaps to enable epidemiological analyses of future disease emergence events

Rapid epidemic expansion of the SARS-CoV-2 Omicron variant in southern Africa

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) epidemic in southern Africa has been characterised by three distinct waves. The first was associated with a mix of SARS-CoV-2 lineages, whilst the second and third waves were driven by the Beta and Delta variants, respectively1-3. In November 2021, genomic surveillance teams in South Africa and Botswana detected a new SARS-CoV-2 variant associated with a rapid resurgence of infections in Gauteng Province, South Africa. Within three days of the first genome being uploaded, it was designated a variant of concern (Omicron) by the World Health Organization and, within three weeks, had been identified in 87 countries. The Omicron variant is exceptional for carrying over 30 mutations in the spike glycoprotein, predicted to influence antibody neutralization and spike function4. Here, we describe the genomic profile and early transmission dynamics of Omicron, highlighting the rapid spread in regions with high levels of population immunity

Gaps in mobility data and implications for modelling epidemic spread: A scoping review and simulation study

Reliable estimates of human mobility are important for understanding the spatial spread of infectious diseases and the effective targeting of control measures. However, when modelling infectious disease dynamics, data on human mobility at an appropriate temporal or spatial resolution are not always available, leading to the common use of model-derived mobility proxies. In this study we reviewed the different data sources and mobility models that have been used to characterise human movement in Africa. We then conducted a simulation study to better understand the implications of using human mobility proxies when predicting the spatial spread and dynamics of infectious diseases.We found major gaps in the availability of empirical measures of human mobility in Africa, leading to mobility proxies being used in place of data. Empirical data on subnational mobility were only available for 17/54 countries, and in most instances, these data characterised long-term movement patterns, which were unsuitable for modelling the spread of pathogens with short generation times (time between infection of a case and their infector). Results from our simulation study demonstrated that using mobility proxies can have a substantial impact on the predicted epidemic dynamics, with complex and non-intuitive biases. In particular, the predicted times and order of epidemic invasion, and the time of epidemic peak in different locations can be underestimated or overestimated, depending on the types of proxies used and the country of interest.Our work underscores the need for regularly updated empirical measures of population movement within and between countries to aid the prevention and control of infectious disease outbreaks. At the same time, there is a need to establish an evidence base to help understand which types of mobility data are most appropriate for describing the spread of emerging infectious diseases in different settings

Model-based projections of Zika virus infections in childbearing women in the Americas

ika virus is a mosquito-borne pathogen that is rapidly spreading across the Americas. Due to associations between Zika virus infection and a range of fetal maladies1,2, the epidemic trajectory of this viral infection poses a significant concern for the nearly 15 million children born in the Americas each year. Ascertaining the portion of this population that is truly at risk is an important priority. One recent estimate3 suggested that 5.42 million childbearing women live in areas of the Americas that are suitable for Zika occurrence. To improve on that estimate, which did not take into account the protective effects of herd immunity, we developed a new approach that combines classic results from epidemiological theory with seroprevalence data and highly spatially resolved data about drivers of transmission to make location-specific projections of epidemic attack rates. Our results suggest that 1.65 (1.45–2.06) million childbearing women and 93.4 (81.6–117.1) million people in total could become infected before the first wave of the epidemic concludes. Based on current estimates of rates of adverse fetal outcomes among infected women2,4,5, these results suggest that tens of thousands of pregnancies could be negatively impacted by the first wave of the epidemic. These projections constitute a revised upper limit of populations at risk in the current Zika epidemic, and our approach offers a new way to make rapid assessments of the threat posed by emerging infectious diseases more generally

Global risk mapping for major diseases transmitted by Aedes aegypti and Aedes albopictus

Objectives: The objective of this study was to map the global risk of the major arboviral diseases transmitted by Aedes aegypti and Aedes albopictus by identifying areas where the diseases are reported, either through active transmission or travel-related outbreaks, as well as areas where the diseases are not currently reported but are nonetheless suitable for the vector. Methods: Data relating to five arboviral diseases (Zika, dengue fever, chikungunya, yellow fever, and Rift Valley fever (RVF)) were extracted from some of the largest contemporary databases and paired with data on the known distribution of their vectors, A. aegypti and A. albopictus. The disease occurrence data for the selected diseases were compiled from literature dating as far back as 1952 to as recent as 2017. The resulting datasets were aggregated at the country level, except in the case of the USA, where state-level data were used. Spatial analysis was used to process the data and to develop risk maps. Results: Out of the 250 countries/territories considered, 215 (86%) are potentially suitable for the survival and establishment of A. aegypti and/or A. albopictus. A. albopictus has suitability foci in 197 countries/territories, while there are 188 that are suitable for A. aegypti. There is considerable variation in the suitability range among countries/territories, but many of the tropical regions of the world provide high suitability over extensive areas. Globally, 146 (58.4%) countries/territories reported at least one arboviral disease, while 123 (49.2%) reported more than one of the above diseases. The overall numbers of countries/territories reporting autochthonous vector-borne occurrences of Zika, dengue, chikungunya, yellow fever, and RVF, were 85, 111, 106, 43, and 39, respectively. Conclusions: With 215 countries/territories potentially suitable for the most important arboviral disease vectors and more than half of these reporting cases, arboviral diseases are indeed a global public health threat. The increasing proportion of reports that include multiple arboviral diseases highlights the expanding range of their common transmission vectors. The shared features of these arboviral diseases should motivate efforts to combine interventions against these diseases

A comprehensive database of the geographic spread of past human Ebola outbreaks

Ebola is a zoonotic filovirus that has the potential to cause outbreaks of variable magnitude in human populations. This database collates our existing knowledge of all known human outbreaks of Ebola for the first time by extracting details of their suspected zoonotic origin and subsequent human-to-human spread from a range of published and non-published sources. In total, 22 unique Ebola outbreaks were identified, composed of 117 unique geographic transmission clusters. Details of the index case and geographic spread of secondary and imported cases were recorded as well as summaries of patient numbers and case fatality rates. A brief text summary describing suspected routes and means of spread for each outbreak was also included. While we cannot yet include the ongoing Guinea and DRC outbreaks until they are over, these data and compiled maps can be used to gain an improved understanding of the initial spread of past Ebola outbreaks and help evaluate surveillance and control guidelines for limiting the spread of future epidemic