Redefining pandemic preparedness: Multidisciplinary insights from the CERP modelling workshop in infectious diseases, workshop report

In July 2023, the Center of Excellence in Respiratory Pathogens organized a two-day workshop on infectious diseases modelling and the lessons learnt from the Covid-19 pandemic. This report summarizes the rich discussions that occurred during the workshop. The workshop participants discussed multisource data integration and highlighted the benefits of combining traditional surveillance with more novel data sources like mobility data, social media, and wastewater monitoring. Significant advancements were noted in the development of predictive models, with examples from various countries showcasing the use of machine learning and artificial intelligence in detecting and monitoring disease trends. The role of open collaboration between various stakeholders in modelling was stressed, advocating for the continuation of such partnerships beyond the pandemic. A major gap identified was the absence of a common international framework for data sharing, which is crucial for global pandemic preparedness. Overall, the workshop underscored the need for robust, adaptable modelling frameworks and the integration of different data sources and collaboration across sectors, as key elements in enhancing future pandemic response and preparedness.

COVID-19 case rates, spatial mobility, and neighbourhood socioeconomic characteristics in Toronto: a spatial-temporal analysis

OBJECTIVES

This study has two primary research objectives: (1) to investigate the spatial clustering pattern of mobility reductions and COVID-19 cases in Toronto and their relationships with marginalized populations, and (2) to identify the most relevant socioeconomic characteristics that relate to human mobility and COVID-19 case rates in Toronto's neighbourhoods during five distinct time periods of the pandemic.

METHODS

Using a spatial-quantitative approach, we combined hot spot analyses, Pearson correlation analyses, and Wilcoxon two-sample tests to analyze datasets including COVID-19 cases, a mobile device-derived indicator measuring neighbourhood-level time away from home (i.e., mobility), and socioeconomic data from 2016 census and Ontario Marginalization Index. Temporal variations among pandemic phases were examined as well.

RESULTS

The paper identified important spatial clustering patterns of mobility reductions and COVID-19 cases in Toronto, as well as their relationships with marginalized populations. COVID-19 hot spots were in more materially deprived neighbourhood clusters that had more essential workers and people who spent more time away from home. While the spatial pattern of clusters of COVID-19 cases and mobility shifted slightly over time, the group socioeconomic characteristics that clusters shared remained similar in all but the first time period. A series of maps and visualizations were created to highlight the dynamic spatiotemporal patterns.

CONCLUSION

Toronto's neighbourhoods have experienced the COVID-19 pandemic in significantly different ways, with hot spots of COVID-19 cases occurring in more materially and racially marginalized communities that are less likely to reduce their mobility. The study provides solid evidence in a Canadian context to enhance policy making and provide a deeper understanding of the social determinants of health in Toronto during the COVID-19 pandemic.

Using spatial and population mobility models to inform outbreak response approaches in the Ebola affected area, Democratic Republic of the Congo, 2018-2020

The Democratic Republic of the Congo's (DRC) 10th known Ebola virus disease (EVD) outbreak occurred between August 1, 2018 and June 25, 2020, and was the largest EVD outbreak in the country's history. During this outbreak, the DRC Ministry of Health initiated traveller health screening at points of control (POC, locations not on the border) and points of entry (POE) to minimize disease translocation via ground and air travel. We sought to develop a model-based approach that could be applied in future outbreaks to inform decisions for optimizing POC and POE placement, and allocation of resources more broadly, to mitigate the risk of disease translocation associated with ground-level population mobility. We applied a parameter-free mobility model, the radiation model, to estimate likelihood of ground travel between selected origin locations (including Beni, DRC) and surrounding population centres, based on population size and drive-time. We then performed a road network route analysis and included estimated population movement results to calculate the proportionate volume of travellers who would move along each road segment; this reflects the proportion of travellers that could be screened at a POC or POE. For Beni, the road segments estimated to have the highest proportion of travellers that could be screened were part of routes into Uganda and Rwanda. Conversely, road segments that were part of routes to other population centres within the DRC were estimated to have relatively lower proportions. We observed a posteriori that, in many instances, our results aligned with locations that were selected for actual POC or POE placement through more time-consuming methods. This study has demonstrated that mobility models and simple spatial techniques can help identify potential locations for health screening at newly placed POC or existing POE during public health emergencies based on expected movement patterns. Importantly, we have provided methods to estimate the proportionate volume of travellers that POC or POE screening measures would assess based on their location. This is critical information in outbreak situations when timely decisions must be made to implement public health interventions that reach the most individuals across a network.

Estimating the size of the monkeypox virus outbreak in Nigeria and implications for global control

BACKGROUND

A multi-country outbreak caused by monkeypox virus (MPXV) has been unfolding across endemic and non-endemic countries since May 2022. Throughout April and May 2022, Nigeria reported 31 MPXV cases, of which 11 were confirmed via testing. In May 2022, three internationally exported cases of MPXV, presumed to have originated in Nigeria, were reported, suggesting that a larger than reported outbreak might be occurring in the country.

METHODS

We used previously established methods to estimate the true size of the MPXV outbreak in Nigeria. We estimated the incidence rate of exported MPXV cases among all outbound international air travellers from Nigeria during the time period of April and May 2022, using forecasted air traveller volumes. We then applied this incidence rate to the entire population of Nigeria during April and May 2022 assuming that the rate of infection was the same in Nigeria for both travellers and the resident population. Information on the subset of population that were considered to be travellers was obtained from the United Nations World Tourism Organization (UNWTO).

RESULTS

We estimated that there were approximately 4000 (N = 4013; 95% CI: 828-11 728) active cases of MPXV in Nigeria in April and May 2022. This is approximately 360-fold greater than the confirmed number and approximately 130-fold greater than the reported number of cases in Nigeria.

CONCLUSION

Our findings suggest that a larger outbreak than is appreciated may be ongoing in Nigeria. The observed international spread of MPXV offers important insights into the scale of the epidemic at its origin, where clinical detection and disease surveillance may be limited. These findings highlight the need to expand and support clinical, laboratory, and public health capacity to enable earlier detection of epidemics of international significance.

Identifying importation points of the SARS-CoV-2 Omicron variant into the USA and potential locations of early domestic spread and impact

Highlight: We identified the US airports and metropolitan areas, particularly New York City, Miami and Los Angeles, that were the most likely locations of importation and domestic spread of Omicron from South Africa. Vaccination coverage suggested that several cities in GA, TX and UT were particularly vulnerable to public health impacts.

Development of a global infectious disease activity database using natural language processing, machine learning, and human expertise

OBJECTIVE

We assessed whether machine learning can be utilized to allow efficient extraction of infectious disease activity information from online media reports.

MATERIALS AND METHODS

We curated a data set of labeled media reports (n = 8322) indicating which articles contain updates about disease activity. We trained a classifier on this data set. To validate our system, we used a held out test set and compared our articles to the World Health Organization Disease Outbreak News reports.

RESULTS

Our classifier achieved a recall and precision of 88.8% and 86.1%, respectively. The overall surveillance system detected 94% of the outbreaks identified by the WHO covered by online media (89%) and did so 43.4 (IQR: 9.5-61) days earlier on average.

DISCUSSION

We constructed a global real-time disease activity database surveilling 114 illnesses and syndromes. We must further assess our system for bias, representativeness, granularity, and accuracy.

CONCLUSION

Machine learning, natural language processing, and human expertise can be used to efficiently identify disease activity from digital media reports.

Potential for inter-state spread of Covid-19 from Arizona, USA: analysis of mobile device location and commercial flight data

A significant rise of SARS-CoV-2 transmission in Arizona in June 2020 prompted the need to evaluate potential dispersion to other regions in the United States. We evaluate the potential for domestic dissemination of SARS-CoV-2 from Arizona using mobile device-location and scheduled flights data.

Pneumonia of unknown aetiology in Wuhan, China: potential for international spread via commercial air travel

There is currently an outbreak of pneumonia of unknown aetiology in Wuhan, China. Although there are still several unanswered questions about this infection, we evaluate the potential for international dissemination of this disease via commercial air travel should the outbreak continue.

Pneumonia of unknown aetiology in Wuhan, China: potential for international spread via commercial air travel

There is currently an outbreak of pneumonia of unknown aetiology in Wuhan, China. Although there are still several unanswered questions about this infection, we evaluate the potential for international dissemination of this disease via commercial air travel should the outbreak continue.

Potential for global spread of a novel coronavirus from China

An epidemic of a novel coronavirus emerged from Wuhan, China, in late December 2019 and has since spread to several large Chinese cities. Should a scenario arise where this coronavirus spreads more broadly across China, we evaluate how patterns of international disease transmission could change.

Infectious disease implications of large-scale migration of Venezuelan nationals

Background

The ongoing economic and political crisis in Venezuela has resulted in a collapse of the healthcare system and the re-emergence of previously controlled or eliminated infectious diseases. There has also been an exodus of Venezuelan international migrants in response to the crisis. We sought to describe the infectious disease risks faced by Venezuelan nationals and assess the international mobility patterns of the migrant population.

Methods

We synthesized data on recent infectious disease events in Venezuela and among international migrants from Venezuela, as well as on current country of residence among the migrant population. We used passenger-level itinerary data from the International Air Transport Association to evaluate trends in outbound air travel from Venezuela over time. We used two parameter-free mobility models, the radiation and impedance models, to estimate the expected population flows from Venezuelan cities to other major Latin American and Caribbean cities.

Results

Outbreaks of measles, diphtheria and malaria have been reported across Venezuela and other diseases, such as HIV and tuberculosis, are resurgent. Changes in migration in response to the crisis are apparent, with an increase in Venezuelan nationals living abroad, despite an overall decline in the number of outbound air passengers. The two models predicted different mobility patterns, but both highlighted the importance of Colombian cities as destinations for migrants and also showed that some migrants are expected to travel large distances. Despite the large distances that migrants may travel internationally, outbreaks associated with Venezuelan migrants have occurred primarily in countries proximate to Venezuela.

Conclusions

Understanding where international migrants are relocating is critical, given the association between human mobility and the spread of infectious diseases. In data-limited situations, simple models can be useful for providing insights into population mobility and may help identify areas likely to receive a large number of migrants.