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2nd Edition: Infectious Disease Modeling in the Era of Complex Data

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A special issue of International Journal of Environmental Research and Public Health (ISSN 1660-4601). This special issue belongs to the section "Infectious Disease Epidemiology".

Deadline for manuscript submissions: closed (30 April 2023) | Viewed by 17586

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Special Issue Editors

Prof. Dr. Elena N. Naumova

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Guest Editor

Division of Nutrition Epidemiology and Data Sciences, Freidman School of Nutrition Science and Policy, Tufts University, 150 Harrison Avenue, Boston, MA 02111, USA
Interests: development of statistical, mathematical and computational models for climate-sensitive infectious diseases; the use of big data, novel information sources and tools, including GIS and remote sensing in public health applications and environmental research
Special Issues, Collections and Topics in MDPI journals

Dr. Tania M. Alarcon Falconi

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Guest Editor

Division of Nutrition Data Sciences, Freidman School of Nutrition Science and Policy, Tufts University, 150 Harrison Avenue, Boston, MA 02111, USA
Interests: development of statistical, mathematical and computational models for climate-sensitive infectious diseases; the use of novel data sources, including GIS and remote sensing in public health applications and environmental research
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With the recent explosion of open data sources, novel analytical tools, increased computation and visualization capacities, and public interest in data-related topics, new opportunities have emerged to better understand the nature, drivers, and implications of emerging and re-emerging infections, especially infections notorious for their strong seasonal patterns, sensitivity to climate and weather fluctuations, and links to environmental drivers. Seasonal patterns in infectious diseases have been long recognized by physicians, epidemiologists, environmental scientists, and public health professionals. This phenomenon has been observed in many environmental, biological and social processes. Yet pressing questions still remain, such as: What is needed to detect flu peaks before the season starts? What can be learned from developing local and global calendars of infection? What can be done to break the transmission of hospital-acquired infections? What are the likely routes of foodborne outbreaks in a given season? How we can build a comprehensive understanding of new type of algorithmic biases that emerge as we compile, fuse, and assemble time-referenced data from many sources? We hope to stir the discussion on how data scientists, modelers, statisticians and forecasters working together with domain experts have to rethink and reframe the state-of-the-art methodology to enable the discovery of emerging trends in infectious diseases.

This Special Issue highlights the advancements and challenges of quantifying important features in disease dynamics. We aim to provide a broad range of examples where the knowledge of temporal dynamics, environmental factors, and seasonal characteristics, such as peak timing, seasonal amplitudes, duration and onset, are crucial for the journal’s broad readership. We especially encourage the submission of interdisciplinary work and multi-country collaborative research. We also encourage the submission of health policy-related manuscripts that focus on issues related to infectious disease dynamics and their relationship to the environment, and recommendations for improving local and global surveillance systems. We welcome original research papers using different study designs as well as systematic reviews and meta-analysis.

Prof. Dr. Elena Naumova
Dr. Tania M. Alarcon Falconi
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. International Journal of Environmental Research and Public Health is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2500 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

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Pleaes visit the special issue first edition by the link below:

https://www.mdpi.com/journal/ijerph/special_issues/Infectious_Disease_Modeling

Published Papers (8 papers)

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Research

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17 pages, 1208 KiB

Open AccessArticle

Ecogeographic Drivers of the Spatial Spread of Highly Pathogenic Avian Influenza Outbreaks in Europe and the United States, 2016–Early 2022

by Jonathon D. Gass, Jr., Nichola J. Hill, Lambodhar Damodaran, Elena N. Naumova, Felicia B. Nutter and Jonathan A. Runstadler

Int. J. Environ. Res. Public Health 2023, 20(11), 6030; https://doi.org/10.3390/ijerph20116030 - 01 Jun 2023

Cited by 2 | Viewed by 2569

Abstract

H5Nx highly pathogenic avian influenza (HPAI) viruses of clade 2.3.4.4 have caused outbreaks in Europe among wild and domestic birds since 2016 and were introduced to North America via wild migratory birds in December 2021. We examined the spatiotemporal extent of HPAI viruses across continents and characterized ecological and environmental predictors of virus spread between geographic regions by constructing a Bayesian phylodynamic generalized linear model (phylodynamic-GLM). The findings demonstrate localized epidemics of H5Nx throughout Europe in the first several years of the epizootic, followed by a singular branching point where H5N1 viruses were introduced to North America, likely via stopover locations throughout the North Atlantic. Once in the United States (US), H5Nx viruses spread at a greater rate between US-based regions as compared to prior spread in Europe. We established that geographic proximity is a predictor of virus spread between regions, implying that intercontinental transport across the Atlantic Ocean is relatively rare. An increase in mean ambient temperature over time was predictive of reduced H5Nx virus spread, which may reflect the effect of climate change on declines in host species abundance, decreased persistence of the virus in the environment, or changes in migratory patterns due to ecological alterations. Our data provide new knowledge about the spread and directionality of H5Nx virus dispersal in Europe and the US during an actively evolving intercontinental outbreak, including predictors of virus movement between regions, which will contribute to surveillance and mitigation strategies as the outbreak unfolds, and in future instances of uncontained avian spread of HPAI viruses. Full article

(This article belongs to the Special Issue 2nd Edition: Infectious Disease Modeling in the Era of Complex Data)

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15 pages, 3087 KiB

Open AccessArticle

Epidemic Diffusion Network of Spain: A Mobility Model to Characterize the Transmission Routes of Disease

by Javier Del-Águila-Mejía, David García-García, Ayelén Rojas-Benedicto, Nicolás Rosillo, María Guerrero-Vadillo, Marina Peñuelas, Rebeca Ramis, Diana Gómez-Barroso and Juan de Mata Donado-Campos

Int. J. Environ. Res. Public Health 2023, 20(5), 4356; https://doi.org/10.3390/ijerph20054356 - 28 Feb 2023

Cited by 1 | Viewed by 1158

Abstract

Human mobility drives the geographical diffusion of infectious diseases at different scales, but few studies focus on mobility itself. Using publicly available data from Spain, we define a Mobility Matrix that captures constant flows between provinces by using a distance-like measure of effective distance to build a network model with the 52 provinces and 135 relevant edges. Madrid, Valladolid and Araba/Álaba are the most relevant nodes in terms of degree and strength. The shortest routes (most likely path between two points) between all provinces are calculated. A total of 7 mobility communities were found with a modularity of 63%, and a relationship was established with a cumulative incidence of COVID-19 in 14 days (CI14) during the study period. In conclusion, mobility patterns in Spain are governed by a small number of high-flow connections that remain constant in time and seem unaffected by seasonality or restrictions. Most of the travels happen within communities that do not completely represent political borders, and a wave-like spreading pattern with occasional long-distance jumps (small-world properties) can be identified. This information can be incorporated into preparedness and response plans targeting locations that are at risk of contagion preventively, underscoring the importance of coordination between administrations when addressing health emergencies. Full article

(This article belongs to the Special Issue 2nd Edition: Infectious Disease Modeling in the Era of Complex Data)

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11 pages, 789 KiB

Open AccessFeature PaperArticle

Passive Surveillance of Human-Biting Ixodes scapularis Ticks in Massachusetts from 2015–2019

by Alexandra Sack, Elena N. Naumova, Lori Lyn Price, Guang Xu and Stephen M. Rich

Int. J. Environ. Res. Public Health 2023, 20(5), 4306; https://doi.org/10.3390/ijerph20054306 - 28 Feb 2023

Cited by 1 | Viewed by 1683

Abstract

This study aimed to analyze human-biting Ixodes scapularis ticks submitted to TickReport tick testing service from 2015–2019 in Massachusetts to (1) examine possible patterns of pathogen-positive adult and nymphal ticks over time and (2) explore how socioeconomic factors can influence tick submissions. A passive surveillance data set of ticks and tick-borne pathogens was conducted over 5 years (2015–2019) in Massachusetts. The percentages of four tick-borne pathogens: Borrelia burgdorferi, Anaplasma phagocytophilum, Babesia microti, and Borrelia miyamotoi were determined by Massachusetts county and by month and year. Regression models were used to examine the association between zip-code-level socioeconomic factors and submissions. A total of 13,598 I. scapularis ticks were submitted to TickReport from Massachusetts residents. The infection rate of B. burgdorferi, A. phagocytophilum, and B. microti was 39%, 8%, and 7% in adult ticks; 23%, 6%, and 5% in nymphal ticks, respectively. A relatively higher level of education was associated with high tick submission. Passive surveillance of human-biting ticks and associated pathogens is important for monitoring tick-borne diseases, detecting areas with potentially high risks, and providing public information. Socioeconomic factors should be considered to produce more generalizable passive surveillance data and to target potentially underserved areas. Full article

(This article belongs to the Special Issue 2nd Edition: Infectious Disease Modeling in the Era of Complex Data)

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11 pages, 9764 KiB

Open AccessArticle

Adapting a Physical Earthquake-Aftershock Model to Simulate the Spread of COVID-19

by Thanushika Gunatilake and Stephen A. Miller

Int. J. Environ. Res. Public Health 2022, 19(24), 16527; https://doi.org/10.3390/ijerph192416527 - 09 Dec 2022

Cited by 1 | Viewed by 1048

Abstract

There exists a need for a simple, deterministic, scalable, and accurate model that captures the dominant physics of pandemic propagation. We propose such a model by adapting a physical earthquake/aftershock model to COVID-19. The aftershock model revealed the physical basis for the statistical Epidemic Type Aftershock Sequence (ETAS) model as a highly non-linear diffusion process, thus permitting a grafting of the underlying physical equations into a formulation for calculating infection pressure propagation in a pandemic-type model. Our model shows that the COVID-19 pandemic propagates through an analogous porous media with hydraulic properties approximating beach sand and water. Model results show good correlations with reported cumulative infections for all cases studied. In alphabetical order, these include Austria, Belgium, Brazil, France, Germany, Italy, New Zealand, Melbourne (AU), Spain, Sweden, Switzerland, the UK, and the USA. Importantly, the model is predominantly controlled by one parameter (

α

), which modulates the societal recovery from the spread of the virus. The obtained recovery times for the different pandemic waves vary considerably from country to country and are reflected in the temporal evolution of registered infections. These results provide an intuition-based approach to designing and implementing mitigation measures, with predictive capabilities for various mitigation scenarios. Full article

(This article belongs to the Special Issue 2nd Edition: Infectious Disease Modeling in the Era of Complex Data)

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14 pages, 2549 KiB

Open AccessArticle

Indirect Virus Transmission via Fomites Can Counteract Lock-Down Effectiveness

by Torsten Thalheim, Tyll Krüger and Jörg Galle

Int. J. Environ. Res. Public Health 2022, 19(21), 14011; https://doi.org/10.3390/ijerph192114011 - 27 Oct 2022

Cited by 1 | Viewed by 1318

Abstract

The spread of severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) has raised major health policy questions. Direct transmission via respiratory droplets seems to be the dominant route of its transmission. However, indirect transmission via shared contact of contaminated objects may also occur. The contribution of each transmission route to epidemic spread might change during lock-down scenarios. Here, we simulate viral spread of an abstract epidemic considering both routes of transmission by use of a stochastic, agent-based SEIR model. We show that efficient contact tracing (CT) at a high level of incidence can stabilize daily cases independently of the transmission route long before effects of herd immunity become relevant. CT efficacy depends on the fraction of cases that do not show symptoms. Combining CT with lock-down scenarios that reduce agent mobility lowers the incidence for exclusive direct transmission scenarios and can even eradicate the epidemic. However, even for small fractions of indirect transmission, such lockdowns can impede CT efficacy and increase case numbers. These counterproductive effects can be reduced by applying measures that favor distancing over reduced mobility. In summary, we show that the efficacy of lock-downs depends on the transmission route. Our results point to the particular importance of hygiene measures during mobility lock-downs. Full article

(This article belongs to the Special Issue 2nd Edition: Infectious Disease Modeling in the Era of Complex Data)

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10 pages, 755 KiB

Open AccessArticle

Five-Year Trend of Measles and Its Associated Factors in Pahang, Malaysia: A Population-Based Study

by Mohd Rujhan Hadfi Mat Daud, Nor Azwany Yaacob, Mohd Ismail Ibrahim and Wan Abdul Rahim Wan Muhammad

Int. J. Environ. Res. Public Health 2022, 19(13), 8017; https://doi.org/10.3390/ijerph19138017 - 30 Jun 2022

Cited by 2 | Viewed by 3270

Abstract

Measles is a disease that has resurfaced as a public health concern in Malaysia. Malaysia has had a Measles Elimination Program in place since 2004, but the incidence of measles in Pahang has not improved significantly. The purpose of this study was to describe the incidence and trend of measles in Pahang, as well as to identify the risk factors. The five-year population-based surveillance data of the entire Pahang state was extracted and analyzed for the trend and incidence of measles from January 2016 to December 2020. Multiple logistic regression was used to examine the relationship between measles and available sociodemographic data. A total of 2844 reported measles cases were investigated. Out of the measles cases reported, 7.41 percent were confirmed. According to the five-year fluctuating trend, the incidence rate ranges from 13.51 to 50.97 per 1,000,000 population. Confirmed measles was significantly associated with an indigenous background (AdjOR = 4.90, 95% CI: 1.74, 13.78), history of contact with measles cases (AdjOR = 14.03, 95% CI: 8.23, 23.90), and incomplete vaccination (AdjOR = 3.38, 95% CI: 2.28, 5.01). In conclusion, the incidence of measles in Pahang remains sporadic, owing to sporadic outbreaks. Vaccination is an important preventive measure that must reach out to the isolated populations such as indigenous people. Full article

(This article belongs to the Special Issue 2nd Edition: Infectious Disease Modeling in the Era of Complex Data)

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11 pages, 1332 KiB

Open AccessArticle

Factors That Prevent Mosquito-Borne Diseases among Migrant Workers in Taiwan: Application of the Health Belief Model in a Church-Based Health Promotion Study

by Yu-Shan Tai and Hao-Jan Yang

Int. J. Environ. Res. Public Health 2022, 19(2), 787; https://doi.org/10.3390/ijerph19020787 - 11 Jan 2022

Cited by 3 | Viewed by 2053

Abstract

Background: Southeast Asian countries have long been considered epidemic areas for mosquito-borne diseases (MBDs), and most imported cases of infectious diseases in Taiwan are from these areas. Taiwanese migrant workers are mainly of Southeast Asian nationality, and of these, 22% are Filipino. Migrant workers’ knowledge of MBDs and self-protection behaviors are beneficial to disease prevention and treatment. This study aims to understand the effectiveness of a health education intervention (HEI) for Filipino migrant workers in Taiwan and explores the factors affecting preventive practices. Methods: The study was conducted between May to September 2018. Participants were recruited from two Catholic churches in Taichung City. A professional delivered a 30 min HEI in person, and a structured questionnaire was used to acquire and assess participants’ knowledge, health beliefs, and preventive behaviors for MBDs before and after the intervention. Results: A total of 291 participants were recruited. The intervention program showed a positive impact on the migrant worker’s knowledge and the perceived severity, perceived benefits, perceived barriers, and preventive practices. Knowledge, perceived severity, and perceived barriers were factors influencing preventive practices in Filipino migrant workers. Conclusions: The results of this study demonstrated that we can direct our efforts towards three areas: improving foreign migrant workers’ awareness of diseases, emphasizing the severity of the disease, and eliminating possible hindrances in the future. As one example, migrant workers could be proactively provided with routine medical examinations and multilingual health education lectures to improve knowledge and preventive practices to contain the spread MBDs. Full article

(This article belongs to the Special Issue 2nd Edition: Infectious Disease Modeling in the Era of Complex Data)

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Review

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20 pages, 1690 KiB

Open AccessReview

Relative Effectiveness of Cell-Cultured versus Egg-Based Seasonal Influenza Vaccines in Preventing Influenza-Related Outcomes in Subjects 18 Years Old or Older: A Systematic Review and Meta-Analysis

by Joan Puig-Barberà, Sonia Tamames-Gómez, Pedro Plans-Rubio and José María Eiros-Bouza

Int. J. Environ. Res. Public Health 2022, 19(2), 818; https://doi.org/10.3390/ijerph19020818 - 12 Jan 2022

Cited by 13 | Viewed by 3184

Abstract

Avian mutations in vaccine strains obtained from embryonated eggs could impair vaccine effectiveness. We performed a systematic review and meta-analysis of the adjusted relative vaccine effectiveness (arVE) of seed cell-cultured influenza vaccines (ccIV) compared to egg-based influenza vaccines (eIV) in preventing laboratory-confirmed influenza related outcomes (IRO) or IRO by clinical codes, in subjects 18 and over. We completed the literature search in January 2021; applied exclusion criteria, evaluated risk of bias of the evidence, and performed heterogeneity, publication bias, qualitative, quantitative and sensitivity analyses. All estimates were computed using a random approach. International Prospective Register of Systematic Reviews, CRD42021228290. We identified 12 publications that reported 26 adjusted arVE results. Five publications reported 13 laboratory confirmed arVE and seven reported 13 code-ascertained arVE. Nine publications with 22 results were at low risk of bias. Heterogeneity was explained by season. We found a significant 11% (8 to 14%) adjusted arVE favoring ccIV in preventing any IRO in the 2017–2018 influenza season. The arVE was 3% (−2% to 7%) in the 2018–2019 influenza season. We found moderate evidence of a significant advantage of the ccIV in preventing IRO, compared to eIV, in a well-matched A(H3N2) predominant season. Full article

(This article belongs to the Special Issue 2nd Edition: Infectious Disease Modeling in the Era of Complex Data)

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