Effectiveness of early warning systems in the detection of infectious diseases outbreaks: a systematic review

Infodemiology and Infoveillance of the Four Most Widespread Arbovirus Diseases in Italy

The purpose of this observational study was to evaluate the potential epidemiological trend of arboviral diseases most reported in Italy by the dedicated national surveillance system (ISS data) compared to searches on the internet, assessing whether a correlation/association between users' searches in Google and Wikipedia and real cases exists. The study considers a time interval from June 2012 to December 2023. We used the following Italian search terms: "Virus Toscana", "Virus del Nilo occidentale" (West Nile Virus in English), "Encefalite trasmessa da zecche" (Tick Borne encephalitis in English), and "Dengue". We overlapped Google Trends and Wikipedia data to perform a linear regression and correlation analysis. Statistical analyses were performed using Pearson's correlation coefficient (r) or Spearman's rank correlation coefficient (rho) as appropriate. All the correlations between the ISS data and Wikipedia or GT exhibited statistical significance. The correlations were strong for Dengue GT and ISS (rho = 0.71) and TBE GT and ISS (rho = 0.71), while the remaining correlations had values of r and rho between 0.32 and 0.67, showing a moderate temporal correlation. The observed correlations and regression models provide a foundation for future research, encouraging a more nuanced exploration of the dynamics between digital information-seeking behavior and disease prevalence.

Syndromic Surveillance Tracks COVID-19 Cases in University and County Settings: Retrospective Observational Study

Background

Syndromic surveillance represents a potentially inexpensive supplement to test-based COVID-19 surveillance. By strengthening surveillance of COVID-19-like illness (CLI), targeted and rapid interventions can be facilitated that prevent COVID-19 outbreaks without primary reliance on testing.

Objective

This study aims to assess the temporal relationship between confirmed SARS-CoV-2 infections and self-reported and health care provider-reported CLI in university and county settings, respectively.

Methods

We collected aggregated COVID-19 testing and symptom reporting surveillance data from Cornell University (2020-2021) and Tompkins County Health Department (2020-2022). We used negative binomial and linear regression models to correlate confirmed COVID-19 case counts and positive test rates with CLI rate time series, lagged COVID-19 cases or rates, and day of the week as independent variables. Optimal lag periods were identified using Granger causality and likelihood ratio tests.

Results

In modeling undergraduate student cases, the CLI rate (P=.003) and rate of exposure to CLI (P<.001) were significantly correlated with the COVID-19 test positivity rate with no lag in the linear models. At the county level, the health care provider-reported CLI rate was significantly correlated with SARS-CoV-2 test positivity with a 3-day lag in both the linear (P<.001) and negative binomial model (P=.005).

Conclusions

The real-time correlation between syndromic surveillance and COVID-19 cases on a university campus suggests symptom reporting is a viable alternative or supplement to COVID-19 surveillance testing. At the county level, syndromic surveillance is also a leading indicator of COVID-19 cases, enabling quick action to reduce transmission. Further research should investigate COVID-19 risk using syndromic surveillance in other settings, such as low-resource settings like low- and middle-income countries.

Prospective Spatiotemporal Cluster Detection Using SaTScan: Tutorial for Designing and Fine-Tuning a System to Detect Reportable Communicable Disease Outbreaks

Staff at public health departments have few training materials to learn how to design and fine-tune systems to quickly detect acute, localized, community-acquired outbreaks of infectious diseases. Since 2014, the Bureau of Communicable Disease at the New York City Department of Health and Mental Hygiene has analyzed reportable communicable diseases daily using SaTScan. SaTScan is a free software that analyzes data using scan statistics, which can detect increasing disease activity without a priori specification of temporal period, geographic location, or size. The Bureau of Communicable Disease's systems have quickly detected outbreaks of salmonellosis, legionellosis, shigellosis, and COVID-19. This tutorial details system design considerations, including geographic and temporal data aggregation, study period length, inclusion criteria, whether to account for population size, network location file setup to account for natural boundaries, probability model (eg, space-time permutation), day-of-week effects, minimum and maximum spatial and temporal cluster sizes, secondary cluster reporting criteria, signaling criteria, and distinguishing new clusters versus ongoing clusters with additional events. We illustrate how to support health equity by minimizing analytic exclusions of patients with reportable diseases (eg, persons experiencing homelessness who are unsheltered) and accounting for purely spatial patterns, such as adjusting nonparametrically for areas with lower access to care and testing for reportable diseases. We describe how to fine-tune the system when the detected clusters are too large to be of interest or when signals of clusters are delayed, missed, too numerous, or false. We demonstrate low-code techniques for automating analyses and interpreting results through built-in features on the user interface (eg, patient line lists, temporal graphs, and dynamic maps), which became newly available with the July 2022 release of SaTScan version 10.1. This tutorial is the first comprehensive resource for health department staff to design and maintain a reportable communicable disease outbreak detection system using SaTScan to catalyze field investigations as well as develop intuition for interpreting results and fine-tuning the system. While our practical experience is limited to monitoring certain reportable diseases in a dense, urban area, we believe that most recommendations are generalizable to other jurisdictions in the United States and internationally. Additional analytic technical support for detecting outbreaks would benefit state, tribal, local, and territorial public health departments and the populations they serve.

Zoonotic spillover and viral mutations from low and middle-income countries: improving prevention strategies and bridging policy gaps

The increasing frequency of zoonotic spillover events and viral mutations in low and middle-income countries presents a critical global health challenge. Contributing factors encompass cultural practices like bushmeat consumption, wildlife trade for traditional medicine, habitat disruption, and the encroachment of impoverished settlements onto natural habitats. The existing "vaccine gap" in many developing countries exacerbates the situation by allowing unchecked viral replication and the emergence of novel mutant viruses. Despite global health policies addressing the root causes of zoonotic disease emergence, there is a significant absence of concrete prevention-oriented initiatives, posing a potential risk to vulnerable populations. This article is targeted at policymakers, public health professionals, researchers, and global health stakeholders, particularly those engaged in zoonotic disease prevention and control in low and middle-income countries. The article underscores the importance of assessing potential zoonotic diseases at the animal-human interface and comprehending historical factors contributing to spillover events. To bridge policy gaps, comprehensive strategies are proposed that include education, collaborations, specialized task forces, environmental sampling, and the establishment of integrated diagnostic laboratories. These strategies advocate simplicity and unity, breaking down barriers, and placing humanity at the forefront of addressing global health challenges. Such a strategic and mental shift is crucial for constructing a more resilient and equitable world in the face of emerging zoonotic threats.

The Eruptive Fevers at Sixes and Sevens

Sixth Disease (roseola infantum) and its primary causative agent, HHV-6, share names that numerically concur. This article examines and answers the question of whether that correspondence is by design or coincidental by briefly reviewing the history and nomenclature of the HHV viruses and the classic febrile rashes of childhood while highlighting some clinical and microbiologic features of HHV-6 infection.

Laboratory testing efficiency during the COVID pandemic: Findings from the Primary Health Care Corporation laboratories in the State of Qatar

BACKGROUND

Little is known about how the COVID-19 pandemic altered laboratory testing efficiency in the State of Qatar. The aim of this study was to assess laboratory testing efficiency with respect to the total number and proportion of C-reactive protein (CRP), complete blood count (CBC), and comprehensive metabolic panel (CMP) tests completed on time in 2019-2021 in several ordinary and COVID-converted Primary Health Care Corporation (PHCC) health centers across Qatar.

METHODS

Secondary data from 2019 to 2021 were accessed from the PHCC-Clinical Information System center. Six randomly selected centers from three regions of Qatar (Northern, Central, and Western), two of which were COVID-converted, were analyzed.

RESULTS

A total of 404,316 laboratory tests were analyzed. There were decreasing, U-shaped, and inverted-U-shaped patterns in the numbers of tests conducted in different regions between 2019 and 2021 according to test type. The proportion of urgent (STAT) CBC and CMP tests increased from 2019 to 2021, and the proportion of tests completed by COVID-converted health centers increased for CRP and CBC and decreased for CMP between 2019 and 2021. Northern and Western regions in Qatar showed higher efficiency than the Central region with respect to the proportion of STAT tests completed on time in 2019-2021. COVID-converted centers completed fewer STAT CBC tests on time than ordinary centers.

CONCLUSION

Pandemics such as COVID-19 shift the allocation of resources from routine tests to urgent tests, as exemplified by the increase in STAT test proportions in 2019 to 2021. High population densities, as noted in the Central region of Qatar, may require additional resources during pandemics to complete urgent tests more efficiently. The conversion of centers to COVID-converted centers may not necessarily translate into higher urgent test efficiency, as exemplified by the STAT CBC test results.

Present and Future Non-Culture-Based Diagnostics: Stewardship Potentials and Considerations

The medical microbiologist plays a key role in the transition from culture-based to molecular test methods for diagnosis of infectious diseases. They must understand the scientific and technical bases underlying these tests along with their associated benefits and limitations and be able to educate administrators and patient providers on their proper use. Coordination of testing practices between clinical departments and the spectrum of public health and research laboratories is essential to optimize health care delivery.

A Low-Cost Early Warning Method for Infectious Diseases with Asymptomatic Carriers

At the beginning of 2023, the Italian former prime minister, the former health minister and 17 others including the current president of the Lombardy region were placed under investigation on suspicion of aggravated culpable epidemic in connection with the government's response at the start of the COVID-19 pandemic. The charges revolve around the failure by authorities to take adequate measures to prevent the spread of the virus in the Bergamo area, which experienced a significant excess of deaths during the initial outbreak. The aim of this paper is to analyse the pandemic data of Italy and the Lombardy region in the first 10 days of the pandemic, spanning from the 24th of February 2020 to the 4th of March 2020. The objective is to determine whether the use of early warning indicators could have facilitated the identification of a critical increase in infections. This identification, in turn, would have enabled the timely formulation of strategies for pandemic containment, thereby reducing the number of deaths. In conclusion, to translate our findings into practical guidelines, we propose a low-cost early warning method for infectious respiratory diseases with asymptomatic carriers.

Enabling countries to manage outbreaks: statistical, operational, and contextual analysis of the early warning and response system (EWARS-csd) for dengue outbreaks

Introduction

Dengue is currently the fastest-spreading mosquito-borne viral illness in the world, with over half of the world's population living in areas at risk of dengue. As dengue continues to spread and become more of a health burden, it is essential to have tools that can predict when and where outbreaks might occur to better prepare vector control operations and communities' responses. One such predictive tool, the Early Warning and Response System for climate-sensitive diseases (EWARS-csd), primarily uses climatic data to alert health systems of outbreaks weeks before they occur. EWARS-csd uses the robust Distribution Lag Non-linear Model in combination with the INLA Bayesian regression framework to predict outbreaks, utilizing historical data. This study seeks to validate the tool's performance in two states of Colombia, evaluating how well the tool performed in 11 municipalities of varying dengue endemicity levels.

Methods

The validation study used retrospective data with alarm indicators (mean temperature and rain sum) and an outbreak indicator (weekly hospitalizations) from 11 municipalities spanning two states in Colombia from 2015 to 2020. Calibrations of different variables were performed to find the optimal sensitivity and positive predictive value for each municipality.

Results

The study demonstrated that the tool produced overall reliable early outbreak alarms. The median of the most optimal calibration for each municipality was very high: sensitivity (97%), specificity (94%), positive predictive value (75%), and negative predictive value (99%; 95% CI).

Discussion

The tool worked well across all population sizes and all endemicity levels but had slightly poorer results in the highly endemic municipality at predicting non-outbreak weeks. Migration and/or socioeconomic status are factors that might impact predictive performance and should be further evaluated. Overall EWARS-csd performed very well, providing evidence that it should continue to be implemented in Colombia and other countries for outbreak prediction.

Preparing for future outbreaks in Ghana: An overview of current COVID-19, monkeypox, and Marburg disease outbreaks

Amidst the ongoing COVID-19 pandemic, Ghana is currently grappling with simultaneous outbreaks of Marburg virus disease and human monkeypox virus. The coexistence of these outbreaks emphasizes the imperative for a collaborative and global approach to enhance surveillance and expedite case detection. While Ghana has made efforts to respond to these outbreaks, this paper outlines the lessons learned and proposes recommendations in this regard. It is crucial to intensify response efforts at the local, regional, and national levels to effectively contain the spread of these infectious diseases. Therefore, this paper suggests prioritizing the following recommendations as crucial for assisting Ghana in adequately preparing for future outbreaks and safeguarding global public health: strengthening surveillance system through digitization, rapid and effective response; risk communication and community engagement; healthcare system readiness; and research and collaboration. Also, prioritizing building healthy public policies and developing personal skills of health personnel across the country is key for future outbreak response.