Novel data and analyses have had an important role in informing the public health response to the COVID-19 pandemic. Existing surveillance systems were scaled up, and in some instances new systems were developed to meet the challenges posed by the magnitude of the pandemic. We describe the routine and novel data that were used to address urgent public health questions during the pandemic, underscore the challenges in sustainability and equity in data generation, and highlight key lessons learnt for designing scalable data collection systems to support decision making during a public health crisis. As countries emerge from the acute phase of the pandemic, COVID-19 surveillance systems are being scaled down. However, SARS-CoV-2 resurgence remains a threat to global health security; therefore, a minimal cost-effective system needs to remain active that can be rapidly scaled up if necessary. We propose that a retrospective evaluation to identify the cost-benefit profile of the various data streams collected during the pandemic should be on the scientific research agenda.

Since 8th March 2020 up to the time of writing, we have been producing near real-time weekly estimates of SARS-CoV-2 transmissibility and forecasts of deaths due to COVID-19 for all countries with evidence of sustained transmission, shared online. We also developed a novel heuristic to combine weekly estimates of transmissibility to produce forecasts over a 4-week horizon. Here we present a retrospective evaluation of the forecasts produced between 8th March to 29th November 2020 for 81 countries. We evaluated the robustness of the forecasts produced in real-time using relative error, coverage probability, and comparisons with null models. During the 39-week period covered by this study, both the short- and medium-term forecasts captured well the epidemic trajectory across different waves of COVID-19 infections with small relative errors over the forecast horizon. The model was well calibrated with 56.3% and 45.6% of the observations lying in the 50% Credible Interval in 1-week and 4-week ahead forecasts respectively. The retrospective evaluation of our models shows that simple transmission models calibrated using routine disease surveillance data can reliably capture the epidemic trajectory in multiple countries. The medium-term forecasts can be used in conjunction with the short-term forecasts of COVID-19 mortality as a useful planning tool as countries continue to relax public health measures.

Hepatitis E virus is a common cause of acute viral hepatitis. We analyzed reports of hepatitis E outbreaks among forcibly displaced populations in sub-Saharan Africa during 2010-2020. Twelve independent outbreaks occurred, and >30,000 cases were reported. Transmission was attributed to poor sanitation and overcrowding.

Background Though women increasingly make up the majority of medical-school and other science graduates, they remain a minority in academic biomedical settings, where they are less likely to hold leadership positions or be awarded research funding. A major factor is the career breaks that women disproportionately take to see to familial duties. They experience a related, but overlooked, hurdle upon their return: they are often too old to be eligible for ‘early-career researcher' grants and ‘career-development' awards, which are stepping stones to leadership positions in many institutions and which determine the demographics of their hierarchies for decades to come. Though age limits are imposed to protect young applicants from more experienced seniors, they have an unintended side effect of excluding returning workers, still disproportionately women, from the running. Methods In this joint effort by the European Society of Clinical Microbiology and Infectious Diseases, the Federation of European Microbiological Societies, the Infectious Disease Society of America, the International Society for Infectious Diseases and the Swiss Society for Infectious Diseases, we invited all European Congress of Clinical Microbiology and Infectious Diseases-affiliated medical societies and funding bodies to participate in a survey on current ‘early-career' application restrictions and measures taken to provide protections for career breaks. Recommendations The following simple consensus recommendations are geared to funding bodies, academic societies and other organizations for the fair handling of eligibility for early-career awards: 1. Apply a professional, not physiological, age limit to applicants. 2. State clearly in the award announcement that career breaks will be factored into applicants' evaluations such that: • Time absent is time extended: for every full-time equivalent of career break taken, the same full-time equivalent will be extended to the professional age limit. • Opportunity costs will also be taken into account: people who take career breaks risk additional opportunity costs, with work that they did before the career break often being forgotten or poorly documented, particularly in bibliometric accounting. Although there is no standardized metric to measure additional opportunity costs, organizations should (a) keep in mind their existence when judging applicants' submissions, and (b) note clearly in the award announcement that opportunity costs of career breaks are also taken into account. 3. State clearly that further considerations can be undertaken, using more individualized criteria that are specific to the applicant population and the award in question. The working group welcomes feedback so that these recommendations can be improved and updated as needed.

Academics and public health practitioners studying communicable disease dynamics have long advocated for open-access data to better inform risk assessments. During any evolving outbreak, the collection, aggregation, visualisation, and analysis of granular data is paramount to developing appropriate public health interventions.1Desai AN Kraemer MUG Bhatia S et al.Real-time epidemic forecasting: challenges and opportunities.Health Secur. 2019; 17: 268-275Crossref PubMed Scopus (51) Google Scholar The COVID-19 pandemic has underscored the need for this type of information, especially in relation to context (eg, timing and intensity of interventions) and epidemiology (eg, spatially resolved and age-specific case counts). Non-traditional disease surveillance tools, including news media reporting, have disseminated event-based information during past disease outbreaks.2Brownstein JS Freifeld CC Madoff LC Digital disease detection—harnessing the Web for public health surveillance.N Engl J Med. 2009; 360: 2153-2157Crossref PubMed Scopus (512) Google Scholar The current global health crisis has highlighted the additional possibilities that so-called data journalism can offer. While the news media has traditionally reported on events of public health importance, media outlets over the course of the COVID-19 pandemic have also conducted data collation, including detailed summaries of case counts and deaths, data curation, and, in some instances, analysis (table). Although some of these data are available through public health department websites, relevant interpretation and data visualisations by media outlets have provided information on the COVID-19 pandemic to the public in near real time.TableExamples of COVID-19 data collection, visualisation, and analysisDescriptionData sourcesURLFinancial Times: coronavirus trackerThe Financial Times analyses the scale of the COVID-19 outbreak including the collection and analysis of data on excess mortality (ie, numbers of deaths higher than the historical average) across the globeWHO, the COVID Tracking Project, Johns Hopkins University, Our World in Data, US Centers for Disease Control and Prevention, and othershttps://www.ft.com/content/a2901ce8-5eb7-4633-b89c-cbdf5b386938The Economist: tracking COVID-19 excess deaths across countries globallyGlobal excess death trackerHuman Mortality Database, World Mortality Dataset, and EuroMOMOhttps://www.economist.com/graphic-detail/coronavirus-excess-deaths-trackerThe New York TimesThe pandemic's hidden tollExcess deaths during the COVID-19 pandemicData are compiled from official national and municipal data for 24 countrieshttps://www.nytimes.com/interactive/2020/04/21/world/coronavirus-missing-deaths.htmlTracking the coronavirus at US colleges and universitiesCOVID-19 tracker at US colleges and universities; with no national tracking system, and statewide data available only sporadically, colleges have been making their own rules for how to tally infectionsThe New York Times surveyed more than 1900 US colleges and universities for COVID-19 informationhttps://www.nytimes.com/interactive/2020/us/covid-college-cases-tracker.htmlWhat we know about coronavirus cases in K-12 schools so farReporting focused on district-level and statewide COVID-19 case totals for public schools in the USA; the numbers presented are minimums because of differences in reportingState and local health and education agencies or were identified by The Covid Monitor or the National Education Association and independently confirmed by The New York Times; The New York Times directly surveyed every school district in eight states: Colorado, Florida, Georgia, Illinois, Indiana, North Carolina, Texas, and Utahhttps://www.nytimes.com/interactive/2020/09/21/us/covid-schools.htmlHow full are hospital ICUs near you?Occupancy levels in US ICUsUS Department of Health and Human Services (hospital capacity data); US Department of Homeland Security (hospital locations); and the Covid-19 Hospitalization Tracking Project, University of Minnesota Carlson School of Managementhttps://www.nytimes.com/interactive/2020/us/covid-hospitals-near-you.htmlThe Atlantic: the COVID-19 tracking projectCollected, cross-checked, and published COVID-19 data from 56 US states and territories regarding testing, hospitalisation, and patient outcomes, providing ethnic demographic information and data on long-term-care facilities*As of March 7, 2021, The Atlantic is no longer collecting new data.COVID-19 data from websites of US state or territory public health authorities; a public data API provides access to all their data at a national and state levelhttps://covidtracking.comThe Hindu: coronavirus India trackerCollects, aggregates, analyses, and visualises state-level COVID-19 cases, deaths, and testing data from India and globallyBing and Johns Hopkins Universityhttps://www.thehindu.com/coronavirus/Zeit Online: coronavirus in Deutschland und bei Ihnen [coronavirus cases in Germany and globally]Collects, aggregates, analyses, and visualises COVID-19 cases, deaths, patients in ICUs, and vaccinationsRobert Koch Institute, websites of German counties and states, and Johns Hopkins Universityhttps://www.zeit.de/wissen/gesundheit/corona-zahlen-deutschland-neuinfektionen-inzidenz-aktuelle-karteICU=intensive care unit. API=application program interface.* As of March 7, 2021, The Atlantic is no longer collecting new data. Open table in a new tab ICU=intensive care unit. API=application program interface. Before COVID-19 was declared a Public Health Emergency of International Concern, news reports served as key data sources to further understand disease transmission and spread. Academic institutions and researchers assembled early epidemiological data scattered across various news articles to inform risk assessments, forecasts, and policy decisions.3Imai N Dorigatti I Cori A Riley S Ferguson NM Report 1: estimating the potential total number of novel Coronavirus cases in Wuhan City, China. Version 2. Imperial College COVID-19 Response Group.https://doi.org/10.25561/77149Date: Jan 17, 2020Date accessed: July 6, 2021Google Scholar The relative dearth of traditional public health data at the beginning of an epidemic is not a new phenomenon. During the 2014–15 west Africa Ebola outbreak, for example, early epidemiological data were often only available through local and international news media articles.4Cori A Donnelly CA Dorigatti I et al.Key data for outbreak evaluation: building on the Ebola experience.Philos Trans R Soc Lond B Biol Sci. 2017; 37220160371Crossref PubMed Scopus (52) Google Scholar As the COVID-19 pandemic evolved, and in response to epidemiological data gaps, news media outlets began to collect and synthesise data for scenarios involving congregate settings such as schools, large public events, and household transmission. In some cases, media have actively reached out and solicited case counts from their readers—a strategy known as participatory surveillance—effectively recruiting the public back into public health (table). News media outlets have also been among the first to systematically collect, aggregate, and analyse excess death counts. For example, the data behind the Financial Times tracker for COVID-19 excess deaths dates to April, 2020; the tracker is open access, and the code and methodology used to clean, analyse, and present the data are available on GitHub. The Economist and The New York Times have also provided their own analyses on excess deaths (table). As the COVID-19 pandemic has shown, there is an urgent need for real-time data that can inform risk assessments to guide public health interventions. While traditional data collection remains the cornerstone of outbreak response, public health programmes and information technology infrastructure are chronically underfunded in many countries and are not always well positioned to collect contextual information in a flexible manner. This is of particular concern during an outbreak when traditional data sources might lag in reporting cases early on. Another key need for epidemic forecasting and risk assessments is data surrounding non-pharmaceutical interventions such as physical distancing, school closures, and lockdowns.5Blavatnik School of GovernmentUniversity of OxfordCOVID-19 government response tracker.https://www.bsg.ox.ac.uk/research/research-projects/coronavirus-government-response-trackerDate accessed: July 6, 2021Google Scholar Interventions differ regionally and implementation timelines are not often readily disseminated. While non-traditional disease surveillance systems have begun to fill some of these gaps, more can be done. Partnerships between academic research centres and news media should be considered, given their complementary strengths; indeed, collaboration between these entities might mitigate their respective weaknesses as well. While news media can rapidly aggregate and disseminate information, they might be unable to sustain these efforts following the course of an outbreak. Likewise, research centres might be able to continue collating and analysing data long after an outbreak has ended, but might be unable to collect relevant information in a timely manner early in an outbreak. It is also important to note that news media data and data visualisations, while informative, differ from peer-reviewed literature. Divergent incentives, intended audiences, and analytic methodologies can result in very different outputs and conclusions. Supporting collaborations between news media outlets that can provide an expedient data stream and academic institutions that can support targeted analyses could be an important step towards improving outbreak response timeliness in the future. In response to the COVID-19 pandemic, several global epidemiological data collection and harmonisation efforts have been initiated to provide guidance on conforming case definitions, data formatting, and data sharing. As these efforts are further developed, data collected by media outlets could be integrated for use by researchers and policy makers, although regulatory issues surrounding data privacy will need to be addressed. Cross-collaborations between academic groups and the media should be encouraged and the role of the media in curating, analysing, and sharing epidemiological information that is otherwise hard to collect should be recognised. While these efforts should be considered complementary to traditional public health endeavours, the rapid dissemination of accurate, real-time information remains paramount in the face of current and future communicable disease outbreaks. We declare no competing interests.

Abstract From 8th March to 29th November 2020, we produced weekly estimates of SARS-CoV-2 transmissibility and forecasts of deaths due to COVID-19 for 81 countries with evidence of sustained transmission. We also developed a novel heuristic to combine weekly estimates of transmissibility to produce forecasts over a 4-week horizon. We evaluated the robustness of the forecasts using relative error, coverage probability, and comparisons with null models. During the 39-week period covered by this study, both the short- and medium-term forecasts captured well the epidemic trajectory across different waves of COVID-19 infections with small relative errors over the forecast horizon. The model was well calibrated with 56.3\% and 45.6\% of the observations lying in the 50\% Credible Interval in 1-week and 4-week ahead forecasts respectively. We could accurately characterise the overall phase of the epidemic up to 4-weeks ahead in 84.9\% of country-days. The medium-term forecasts can be used in conjunction with the short-term forecasts of COVID-19 mortality as a useful planning tool as countries continue to relax public health measures.

Data from digital disease surveillance tools such as ProMED and HealthMap can complement the field surveillance during ongoing outbreaks. Our aim was to investigate the use of data collected through ProMED and HealthMap in real-time outbreak analysis. We developed a flexible statistical model to quantify spatial heterogeneity in the risk of spread of an outbreak and to forecast short term incidence trends. The model was applied retrospectively to data collected by ProMED and HealthMap during the 2013–2016 West African Ebola epidemic and for comparison, to WHO data. Using ProMED and HealthMap data, the model was able to robustly quantify the risk of disease spread 1–4 weeks in advance and for countries at risk of case importations, quantify where this risk comes from. Our study highlights that ProMED and HealthMap data could be used in real-time to quantify the spatial heterogeneity in risk of spread of an outbreak.

Background As of July 2021, more than 180,000,000 cases of COVID-19 have been reported across the world, with more than 4 million deaths. Mathematical modelling and forecasting efforts have been widely used to inform policy-making and to create situational awareness. Methods and Findings From 8 th March to 29 th November 2020, we produced weekly estimates of SARS-CoV-2 transmissibility and forecasts of deaths due to COVID-19 for countries with evidence of sustained transmission. The estimates and forecasts were based on an ensemble model comprising of three models that were calibrated using only the reported number of COVID-19 cases and deaths in each country. We also developed a novel heuristic to combine weekly estimates of transmissibility and potential changes in population immunity due to infection to produce forecasts over a 4-week horizon. We evaluated the robustness of the forecasts using relative error, coverage probability, and comparisons with null models. Conclusions During the 39-week period covered by this study, we produced short- and medium-term forecasts for 81 countries. Both the short- and medium-term forecasts captured well the epidemic trajectory across different waves of COVID-19 infections with small relative errors over the forecast horizon. The model was well calibrated with 56.3% and 45.6% of the observations lying in the 50% Credible Interval in 1-week and 4-week ahead forecasts respectively. We could accurately characterise the overall phase of the epidemic up to 4-weeks ahead in 84.9% of country-days. The medium-term forecasts can be used in conjunction with the short-term forecasts of COVID-19 mortality as a useful planning tool as countries continue to relax stringent public health measures that were implemented to contain the pandemic.

Background The United Nations Refugee Agency (UNHCR) estimates the number of forcibly displaced people increased from 22.7 million people in 1996 to 67.7 million people in 2016. Human mobility is associated with the introduction of infectious disease pathogens. The aim of this study was to describe the range of pathogens in forcibly displaced populations over time using an informal event monitoring system. Methods We conducted a retrospective analysis of ProMED, a digital disease monitoring system, to identify reports of outbreak events involving forcibly displaced populations between 1996 and 2016. Number of outbreak events per year was tabulated. Each record was assessed to determine outbreak location, pathogen, origin of persons implicated in the outbreak, and suspected versus confirmed case counts. Results One hundred twenty-eight independent outbreak events involving forcibly displaced populations were identified. Over 840,000 confirmed or suspected cases of infectious diseases such as measles, cholera, cutaneous leishmaniasis, dengue, and others were reported in 48 destination countries/territories. The average rate of outbreak events concerning forcibly displaced persons per total number of reports published on ProMED per year increased over time. The majority of outbreak events (63%) were due to acquisition of disease in the destination country. Conclusion This study found that reports of outbreak events involving forcibly displaced populations have increased in ProMED. The events and outbreaks detected in this retrospective review underscore the importance of capturing displaced populations in surveillance systems for rapid detection and response.

OBJECTIVES:The protracted and violent conflict in Syria has resulted in large-scale displacement of people and destruction of health and sanitation infrastructure. The aim of this study was to examine epidemiological trends in vector-borne disease (VBD) outbreaks before and following the onset of the Syrian conflict (2011).METHODS:ProMED, a digital disease surveillance tool, was queried for VBD outbreak reports affecting humans and animals in Syria and select bordering countries between 2003 and 2018. Data were normalized by dividing the number of unique VBD events by the total number of unique outbreak events reported by ProMED for each year. Suspected and confirmed case counts and deaths were manually extracted.RESULTS:Reports on VBDs increased from a mean of 2.9/year pre-2011 to 12.8/year post-2011, a 343.5% (p < 0.05) increase. After normalization, reports increased by 485.5% (p < 0.05) over the time periods. Post-2011, the most commonly reported VBDs were leishmaniasis, Crimean-Congo hemorrhagic fever, and lumpy skin disease. Reported numbers of suspected and confirmed cases and deaths increased during the conflict period.CONCLUSIONS:VBD outbreak events in ProMED increased in Syria and select bordering countries after the onset of the Syrian conflict in 2011. Enhanced disease surveillance is critical to detect and manage outbreaks in conflict settings.

Background: Syria's uprising which escalated into conflict in 2011 has displaced half of the pre-war population of 22 million with most of the 5 million Syrian refugees residing in Turkey, Lebanon, Jordan and Iraq. The aim of this study is to analyze ProMED reports from the region with the aim of understanding the impact of conflict on vector-borne disease (VBD) trends among both humans and animals. ProMED is a digital, global outbreak reporting system that uses formal and informal sources to rapidly publish reports on emerging infectious diseases. Methods and materials: The ProMED search engine was queried for outbreak events occurring in Syria and surrounding countries (Turkey, Lebanon, Jordan, Iraq) between 2003–2018. The latter countries were selected as the main Syrian-refugee hosting countries. Posts were deduplicated and the most recent posting was saved. Suspected and confirmed case counts, species affected, and date of event were manually extracted. Posts included human and animal sources. VBD events were defined using WHO, OIE, and NIAID criteria. Results: 743/1961 (38%) of initial reports were retained, and 125/743 (17%) of all events were due to a VBD. The number of VBD events began increasing annually in 2011 and reached its peak in 2017. A comparison between the eight years before the Syrian Conflict and eight years after its onset revealed an increase in VBD events from an average of 2.9/year to 12.8/year, reflecting a 343% increase (p < 0.05). The three most common VBD during the Syrian Conflict were leishmaniasis (62%), Lumpy Skin Disease, and Crimean-Congo hemorrhagic fever. On a country level, Syria (32), Iraq (30), and Turkey (27) showed the highest numbers of VBD events. Conclusion: This study finds that VBD events increased in Syria and surrounding countries after the onset of the Syrian conflict. This is likely due to the disruption of vector-control mechanisms, the destruction of health and sanitation infrastructure in Syria and the strains on these infrastructures in neighboring countries. These trends support increased attention to vector-control health measures during conflict as well as ongoing, enhanced disease surveillance.

BACKGROUND:Media reporting on communicable diseases has been demonstrated to affect the perception of the public. Communicable disease reporting related to foreign-born persons has not yet been evaluated.OBJECTIVE:Examine how political leaning in the media affects reporting on tuberculosis (TB) in foreign-born persons.METHODS:HealthMap, a digital surveillance platform that aggregates news sources on global infectious diseases, was used. Data was queried for media reports from the U.S. between 2011-2019, containing the term "TB" or "tuberculosis" and "foreign born", "refugee (s)," or "im (migrants)." Reports were reviewed to exclude duplicates and non-human cases. Each media source was rated using two independent media bias indicators to assess political leaning. Forty-six non-tuberculosis reports were randomly sampled and evaluated as a control. Two independent reviewers performed sentiment analysis on each report.RESULTS:Of 891 TB-associated reports in the US, 46 referenced foreign-born individuals, and were included in this analysis. 60.9% (28) of reports were published in right-leaning news media and 6.5% (3) of reports in left-leaning media, while 39.1% (18) of the control group reports were published in left- leaning media and 10.9% (5) in right-leaning media (p < .001). 43% (20) of all study reports were posted in 2016. Sentiment analysis revealed that right-leaning reports often portrayed foreign-born persons negatively.CONCLUSION:Preliminary data from this pilot suggest that political leaning may affect reporting on TB in US foreign-born populations. Right-leaning news organizations produced the most reports on TB, and the majority of these reports portrayed foreign-born persons negatively. In addition, the control group comprised of non-TB, non-foreign born reports on communicable diseases featured a higher percentage of left-leaning news outlets, suggesting that reporting on TB in foreign-born individuals may be of greater interest to right-leaning outlets. Further investigation both in the U.S. and globally is needed.

Background: The purpose of this study was to identify global trends in Listeria monocytogenes epidemiology using ProMED reports. ProMED is a publicly available, global outbreak reporting system that uses both informal and formal sources. In the context of Listeria, ProMED reports on atypical findings such as higher than average case counts, events from unusual sources, and multinational outbreaks. Methods: Keywords "Listeria" and "listeriosis" were utilized in the ProMED search engine covering the years 1996-2018. Issue date, countries involved, source, suspected and confirmed case counts, and fatalities were extracted. Data unique to each event, including commentary by content experts, were evaluated. When multiple reports regarding the same outbreak or recall were obtained, the last report pertaining to that outbreak was utilized. Rates of Listeria events over time were compared using a normal approximation to the Poisson distribution; p < 0.05 was considered to be statistically significant. Results: From 1996 through 2018, 123 Listeria events were identified in the ProMED database. Eighty-one events (65%) were associated with two or more human cases (outbreak events), 13 events (11%) were associated with only one human case (sporadic cases), and 29 events (24%) were precautionary food product recalls due to the presence of bacterial contamination without associated human cases. The implicated food vehicle was identified in 69 (85%) outbreak events and in 10 (77%) sporadic case events. Listeria contaminated foods were identified in all precautionary recall events. Overall, 28 events (23%) implicated novel food vehicles/sources. Events associated with novel food vehicles increased over the study period (p < 0.02), as did international events with more than one country involved (p < 0.02). Ten reports (8%) described hospital-acquired events. Conclusions: This study demonstrates the use of publicly available data to document Listeria epidemiological trends, particularly in settings where foodborne disease surveillance is weak or nonexistent. Over the last decade, an increasing number of events have been associated with foods not traditionally recognized as vehicles for Listeria transmission, and a rise in international events was noted. Informing high-risk individuals such as pregnant women and immunocompromised individuals of safe food handling practices is warranted. To ensure timely recall of contaminated food products, open data sharing and communication across borders is critical. Changes in food production and distribution, and improved diagnostics may have contributed to the observed changes. (C) 2019 The Author(s). Published by Elsevier Ltd on behalf of International Society for Infectious Diseases.

Infectious disease outbreaks play an important role in global morbidity and mortality. Real-time epidemic forecasting provides an opportunity to predict geographic disease spread as well as case counts to better inform public health interventions when outbreaks occur. Challenges and recent advances in predictive modeling are discussed here. We identified data needs in the areas of epidemic surveillance, mobility, host and environmental susceptibility, pathogen transmissibility, population density, and healthcare capacity. Constraints in standardized case definitions and timely data sharing can limit the precision of predictive models. Resource-limited settings present particular challenges for accurate epidemic forecasting due to the lack of granular data available. Incorporating novel data streams into modeling efforts is an important consideration for the future as technology penetration continues to improve on a global level. Recent advances in machine-learning, increased collaboration between modelers, the use of stochastic semi-mechanistic models, real-time digital disease surveillance data, and open data sharing provide opportunities for refining forecasts for future epidemics. Epidemic forecasting using predictive modeling is an important tool for outbreak preparedness and response efforts. Despite the presence of some data gaps at present, opportunities and advancements in innovative data streams provide additional support for modeling future epidemics.

Healthcare-acquired infections cause significant morbidity and mortality, particularly in low- and middle-income settings (Vilar-Compte et al., 2017Vilar-Compte D. Camacho-Ortiz A. Ponce-de-Leon S. Infection control in limited resources countries: challenges and priorities.Curr Infect Dis Rep. 2017; 19: 20Crossref PubMed Scopus (28) Google Scholar). Lack of adequate training underpinning infection prevention and control, lack of defined infection prevention policies and procedures and lack of funding from governments and hospital administrators are of particular concern in these settings. Unsafe water and sanitation facilities, inconsistent surveillance, lack of vaccines, inappropriate use of antimicrobials, poor waste management in hospitals and communities alike, and poor hand hygiene can further contribute to the spread of infections in the healthcare setting. Implementation of adequate infection prevention and control (IPC) measures as well as adequate microbiology laboratory support are critical to preventing, identifying and responding to healthcare acquired infections. In low and middle income countries (LMICs), the status of national IPC programmes varies widely within and between countries, from robust to non-existent IPC structures. The results of a recent survey conducted by ISID, involving more than 1100 healthcare workers from around the world, demonstrated that many disparities exist in the context of IPC between different national resource levels (Desai et al., 2019Desai A.N. Ramatowski J.W. Lassmann B. Holmes A. Mehtar S. Bearman G. Global infection prevention gaps, needs, and utilization of educational resources: a cross-sectional assessment by the International Society for Infectious Diseases.Int J Infect Dis. 2019; 82: 54-60Abstract Full Text Full Text PDF PubMed Scopus (12) Google Scholar). The largest gaps identified in this survey were related to availability of personal protective equipment, availability of basic microbiology laboratory capacity, availability of antimicrobial sensitivity data, and availability of IPC support services. Lack of appropriate infrastructure, capacity and financial constraints were highlighted in other studies as explanations for IPC discrepancies that exist between regions (Bardossy et al., 2016Bardossy A. Zervos J. Zervos M. Preventing hospital-acquired infections in low-income and middle-income countries.Infect Dis Clin North Am. 2016; 30: 805-818Abstract Full Text Full Text PDF PubMed Scopus (36) Google Scholar, Lynch et al., 2007Lynch P. Pittet D. Borg M. Mehtar S. Infection control in countries with limited resources.J Hosp Infect. 2007; 65: 148-150Abstract Full Text PDF PubMed Scopus (30) Google Scholar). It is clear that available evidence-based IPC practices developed in high income countries have encountered barriers to implementation in LMICs. In addition, many resources are difficult to access, navigate and use (Sastry et al., 2017Sastry S. Masroor N. Bearman G. et al.The 17th International Congress on Infectious Diseases workshop on developing infection prevention and control resources for low- and middle-income countries.Int J Infect Dis. 2017; 57: 138-143Abstract Full Text Full Text PDF PubMed Scopus (20) Google Scholar). There is an urgent need to adapt and/or contextualize the available IPC approaches to implementable recommendations which address the reality in LMICs. The International Society for Infectious Diseases (ISID) is a not-for-profit organization founded in 1986 to improve the care of patients with infectious diseases, the professional development and standing of clinicians and scientists in the field, and the control of infectious diseases around the world. Since 1998, the ISID has developed and distributed "A Guide to Infection Control in the Healthcare Setting," which is a free resource dedicated to summarizing the most up-to-date principles, interventions, and strategies for minimizing healthcare associated infections. When the 6th Edition was published online in 2019, Richard Wenzel, the Founding Editor of ISID's Guide stated "I assembled a team of international authors who were enthusiastic supporters of the project, and my only condition was that no author or editor made money from the project and that the Guide would be distributed free to all medical personnel in LMICs. Within a few years, we had increasing requests to translate the Guide into various languages, adding to its value." Currently, more than 70 authors from around the globe volunteer to write, revise and keep content up-to-date. Six Editors volunteer their time and expertise to provide oversight and to ensure content is evidence-based. Past editions of the Guide were translated into Chinese, Croatian, Greek, Polish, Russian, and Spanish. The Guide to Infection Control in the Healthcare Setting is freely available online through ISID's webpage (https://www.isid.org/guide/). The ISID Position Paper Series is an exciting new initiative for the ISID to highlight key issues in international health and infectious diseases and to provide a consensus, developing these recommendations into manuscripts for publication in the International Journal of Infectious Diseases (IJID). The first series is based on content developed for the ISID's 6th Edition of the Guide to Infection Control in the Healthcare Setting. The goal of this first series is to facilitate implementation of IPC measures in low- and middle-income countries and to provide evidence-based or, when evidence is not available, expert consensus recommendations. The IJID is the official publication of the ISID. Position papers are the official opinions and recommendations of the International Society for Infectious Diseases (ISID) and reflect the mission and values of the Society. ISID's Publication Committee is a group of ISID members who, under the leadership of the Committee Chair, provide leadership in the development of position papers for the Society. The chair of ISID's Publication Committee provides oversight of the development and peer-review of position papers and reports directly to the President of the Society. Relevant content for inclusion in the Position Paper series is selected by the ISID Publication Committee in conjunction with ISID's Guide to Infection Control Editors and the Editor-in-Chief of the IJID. To ensure applicability across various resource settings and to reflect ISID's diverse membership, co-authors from various regions and resource levels provide their insights and expertise by contributing to the manuscript. The authoring groups of the ISID Position Papers are required to disclose all conflicts of interest and affiliations with industry. The first ISID Position Papers were published earlier in 2019 with a focus on CLABSI (Lutwick et al., 2019Lutwick L. Al-Maani A.S. Mehtar S. Memish Z. Rosenthal V.D. Dramowski A. et al.Managing and preventing vascular catheter infections: A position paper of the International Society for Infectious Diseases.Int J Infect Dis. 2019; 84: 22-29Abstract Full Text Full Text PDF PubMed Scopus (18) Google Scholar) and Hand Hygiene (Loftus et al., 2019Loftus M.J. Guitart C. Tartari E. Stewardson A.J. Amer F. Bellissimo-Rodrigues F. et al.Hand hygiene in low- and middle-income countries: A position paper of the International Society for Infectious Diseases.Int J Infect Dis. 2019; : 9PubMed Google Scholar).

Purpose: In our increasingly interconnected world, it is crucial to understand the risk of an outbreak originating in one country/region and spreading to the rest of the world. Digital disease surveillance tools such as ProMed, HealthMap etc. can serve as important early warning systems as well as complement field surveillance data during an ongoing outbreak. While there are a number of systems that carry out digital disease surveillance, there is as yet a lack of tools that can compile and analyse the generated data to produce easily understood actionable reports. The purpose of our work is to design and implement a flexible statistical model that uses different streams of data such as disease surveillance data, mobility data etc. for short-term incidence trend forecasting. Methods & Materials: This is a modelling study making use of publicly available data. For incidence trends, we use data from ProMED and HealthMap. Other sources of data are Heathsites.io for information on health facilities and GADM for national and international administrative boundaries. The model will be made available as a R package as well as through a website for use by non-technical stakeholders. Results: We will showcase the use of our model through the analysis of data from the 2014 West African Ebola Epidemic. We show that using only data obtained through digital surveillance (ProMED and HealthMap), we are able to forecast short term incidence trajectory that is consistent with that obtained using field surveillance data. We will also highlight an example of disaggregating aggregated data to obtain incidence information at a fine spatial scale. This could be particularly important in instances where information at sub-national levels is lacking or incomplete. Conclusion: Our work makes two key contributions:a)We provide a realistic appraisal of the strengths and limitations of data collected through digital surveillance in incidence forecasting.b)We infer incidence trends at finer spatial scales from aggregated data. Our work provided an example of the way in which data from digital surveillance systems can complement the data collected from traditional public health infrastructure.