Topic Editors

Department of Nursing, National and Kapodistrian University of Athens, 10679 Athens, Greece
1. Department of Hygiene and Environmental Protection, School of Medicine, Democritus University of Thrace, 68100 Alexandroupolis, Greece
2. Infectious Diseases Service, Department of Medicine, Lausanne University Hospital, 46, 1011 Lausanne, Switzerland

Surveillance Systems and Predictive Analytics for Epidemics

Abstract submission deadline
31 October 2026
Manuscript submission deadline
31 January 2027
Viewed by
2573

Topic Information

Dear Colleagues,

In an interconnected world where diseases can spread rapidly across borders, the ability to detect epidemics promptly is more critical than ever. The COVID-19 pandemic and other recent outbreaks have underscored that early detection can dramatically reduce the impact of an epidemic by enabling swift public health interventions. Modern digital surveillance systems harness advances in technology—ranging from real-time data collection to AI-driven analytics—to monitor emerging health threats worldwide and overcome delays and gaps inherent in traditional reporting. Predictive analytics, including machine learning models and big data approaches, offer a powerful means to anticipate outbreak trends and enhance preparedness, effectively turning surveillance into a proactive tool for response. Together, improved surveillance and predictive insights can significantly strengthen epidemic preparedness and guide more effective responses. Achieving these goals, however, requires broad collaboration across disciplines, including public health experts, epidemiologists, data scientists, and systems engineers, to integrate diverse expertise into robust systems for epidemic surveillance and predictive analytics. This multidisciplinary Topic encourages contributions from experts across these domains and beyond, aiming to foster innovative approaches for early epidemic detection and to improve our collective ability to respond to health crises and future outbreaks, ultimately strengthening global health security.

Dr. Georgia Kourlaba
Dr. Elisavet Stavropoulou
Topic Editors

Keywords

  • outbreak prediction
  • big data analytics
  • surveillance systems
  • machine learning in public health
  • public health preparedness
  • digital health monitoring

Participating Journals

Journal Name Impact Factor CiteScore Launched Year First Decision (median) APC
Diseases
diseases
3.0 3.7 2013 21 Days CHF 1800 Submit
Epidemiologia
epidemiologia
2.2 4.3 2020 21.9 Days CHF 1400 Submit
Infectious Disease Reports
idr
2.4 6.0 2009 34.1 Days CHF 1800 Submit
Medicina
medicina
2.4 4.1 1920 17.5 Days CHF 2200 Submit
Tropical Medicine and Infectious Disease
tropicalmed
2.6 4.7 2016 22.2 Days CHF 2700 Submit

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Published Papers (3 papers)

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18 pages, 3855 KB  
Article
Impact of the COVID-19 Pandemic on Vaccine-Preventable Diseases in Mexico: A Time Series Analysis (2014–2024)
by María Fernanda Hernández-Batres, Sofía Bernal-Silva, Georgina Cristina Delgado-Juárez and Andreu Comas-Garcia
Epidemiologia 2026, 7(1), 26; https://doi.org/10.3390/epidemiologia7010026 - 11 Feb 2026
Viewed by 318
Abstract
The COVID-19 pandemic has significantly impacted public health in Mexico. Background/Objectives: This study evaluated its impact on the frequency of vaccine-preventable diseases (VPDs) from 2020 to 2024. Methods: The analyzed information was extracted from the weekly epidemiological bulletins, which compile the suspected, probable, [...] Read more.
The COVID-19 pandemic has significantly impacted public health in Mexico. Background/Objectives: This study evaluated its impact on the frequency of vaccine-preventable diseases (VPDs) from 2020 to 2024. Methods: The analyzed information was extracted from the weekly epidemiological bulletins, which compile the suspected, probable, and confirmed cases reported to the Ministry of Health. The epidemiological behavior of VPDs was analyzed with endemic channels based on 2014–2019 data. An endemic channel is a graphical tool that is used to plot a central tendency and its limits; with this tool we can detect the presence of an epidemic and quantify it. Between 2020 and 2024, VPDs presented variable patterns due to the pandemic. Results: Rotavirus cases exhibited an 81% negative deviation in 2020 and a final 47% negative deviation in comparison with the expected values from 2014–2019. Chickenpox declined by 91% in 2020, with a partial recovery in reports afterward. Hepatitis A and B declined initially, but hepatitis B surpassed pre-pandemic levels later. Mumps declined by 45% in 2020, with a partial recovery, remaining 35% below expected reports. Meningeal and pulmonary tuberculosis increased by 125% and 33%, respectively. Human Papilloma Virus (HPV) infection and mild cervical dysplasia showed negative deviations, with partial increases later. However, severe dysplasia and in situ cervical cancer reports exceeded expected levels. Conclusions: Overall, several VPDs showed negative deviations, which could increase the size of the susceptible population. In contrast, increases in tuberculosis and HPV infection present a major challenge for health systems, given their chronic and high treatment costs. Full article
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9 pages, 249 KB  
Article
Risk of Cancer in Patients with Rheumatoid Arthritis Compared with the General Population: A Nationwide Cohort Study in Lithuania
by Vaida Gedvilaitė, Augustė Kačėnienė, Gintarė Jonušienė, Jolanta Dadonienė, Dalia Miltinienė, Giedrė Deresevičienė and Giedrė Smailytė
Medicina 2026, 62(2), 290; https://doi.org/10.3390/medicina62020290 - 1 Feb 2026
Viewed by 241
Abstract
Background and Objectives: Rheumatoid arthritis (RA) is a multisystem autoimmune disease that needs immunosuppressive treatment. Previously, studies have shown an increased risk of cancer in patients with RA compared with the general population. The purpose of this study was to explore the [...] Read more.
Background and Objectives: Rheumatoid arthritis (RA) is a multisystem autoimmune disease that needs immunosuppressive treatment. Previously, studies have shown an increased risk of cancer in patients with RA compared with the general population. The purpose of this study was to explore the associations between RA and cancer risk, providing updated insights into the incidence of specific cancers in patients with RA. Materials and Methods: A total of 746 cancer cases were observed, with the most common types being nonmelanoma skin cancer (139 cases), breast cancer (87 cases), lung cancer (47 cases), and Hodgkin lymphoma (43 cases). Results: Compared with the general Lithuanian population, patients with RA had an increased overall cancer risk, with an SIR of 1.17 and 95% CI of 1.09–1.26. Hematological cancers and nonmelanoma skin cancers were the most common types of cancer in the RA population, and patients with RA had a significantly greater risk of site-specific cancers (non-Hodgkin lymphoma: SIR 4.19, 95% CI 1.57–11.18; Hodgkin lymphoma: SIR 3.03, 95% CI 2.11–4.36; myeloma: SIR 3.00, 95% CI 1.84–4.90; leukemia: SIR 2.39, 95% CI 1.62–3.54; and skin nonmelanoma: SIR 1.54, 95% CI 1.27–1.83). Male patients with RA had an increased risk of prostate and kidney cancer (SIR 1.40, 95% CI 1.12–1.75; SIR 1.85, 95% CI 1.11–3.06). Our study revealed a significantly lower risk of colorectal cancer among patients with RA. Additionally, we observed a statistically significant reduction in the risk of mouth and pharynx cancers; however, this finding was based on only three observed cases. Conclusions: Patients with RA remain particularly affected by an increased cancer risk. Knowing these risks, we need clear recommendations for specific screenings in patients with RA, which could allow for early diagnosis and better cancer treatment in the early stages. Full article
19 pages, 3361 KB  
Article
Integrated Surveillance for Human and Animal Brucellosis in Kenya: A Predictive Analysis
by Samuel Kahariri, Lian F. Thomas, Bernard Bett, Marianne W. Mureithi, Anita Makori, Brian Njuguna, Samuel Kadivane, Dennis N. Makau, Nyamai Mutono and S. M. Thumbi
Trop. Med. Infect. Dis. 2025, 10(12), 344; https://doi.org/10.3390/tropicalmed10120344 - 9 Dec 2025
Viewed by 1524
Abstract
Brucellosis is a bacterial zoonotic disease which poses a significant public health challenge globally. In Kenya, it is a priority zoonosis, causing morbidity and losses in humans and animals. Here, we used monthly surveillance data from 2014 to 2022 from the official human [...] Read more.
Brucellosis is a bacterial zoonotic disease which poses a significant public health challenge globally. In Kenya, it is a priority zoonosis, causing morbidity and losses in humans and animals. Here, we used monthly surveillance data from 2014 to 2022 from the official human and animal health surveillance databases. We conducted spatiotemporal analysis, tested associations between human and animal brucellosis using Time Series Linear Models, and forecasted the incidence of human brucellosis for twelve months using Seasonal Autoregressive Integrated Moving Average (SARIMA) models. Our analysis revealed a significant disparity in brucellosis cases, with a much higher cumulative number of human cases (4,688,787) compared to animal cases (1214). Human incidence depicted a relatively stable trend, with occasional fluctuations. However, cattle and camel incidences displayed sporadic peaks and troughs. Only cattle brucellosis was significantly associated (estimate: 0.355; 95% CI: 0.004 to 0.707) with human brucellosis. SARIMA models demonstrated reasonable predictive accuracy for human incidence, but incorporating animal data did not significantly improve model performance. Our study highlights the weaknesses in the existing surveillance systems and the need for comprehensive evaluation of the systems and implementation of integrated surveillance to address gaps in surveillance, improve the accuracy of predictive analysis, and enhance early detection for zoonotic diseases. Full article
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