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Editorial

Editorial: Infectious Disease Epidemiology and Transmission Dynamics

by
Zhanwei Du
1,2,
Wei Luo
3,4,
Rachel Sippy
5 and
Lin Wang
5,*
1
WHO Collaborating Center for Infectious Disease Epidemiology and Control, School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
2
Laboratory of Data Discovery for Health Limited, Hong Kong Science Park, Hong Kong SAR, China
3
Department of Geography, National University of Singapore, Singapore 117570, Singapore
4
Saw Swee Hock School of Public Health, National University of Singapore, Singapore 117570, Singapore
5
Department of Genetics, University of Cambridge, Cambridge CB2 3EH, UK
*
Author to whom correspondence should be addressed.
Viruses 2023, 15(1), 246; https://doi.org/10.3390/v15010246
Submission received: 11 January 2023 / Accepted: 13 January 2023 / Published: 15 January 2023
(This article belongs to the Special Issue Infectious Disease Epidemiology and Transmission Dynamics)
Versions Notes
Infectious diseases, such as COVID-19 [1], influenza [2], dengue [3], and monkeypox [4], have caused significant burdens to population health and socioeconomic development in the world. Scientists have unraveled many aspects as to the transmission dynamics and population health strategies to mitigate the spread of epidemics [5,6,7,8,9,10,11,12,13,14,15]. This Special Issue contains eleven original articles and two commentaries for scientific endeavors that bring together expertise and efforts toward this common goal.
The research includes studies on the reproduction number of SARS-CoV-2 variants, vaccine efficacy, antiviral efficacy, and the epidemiological impact of nonpharmaceutical interventions. Our research mainly focuses on COVID-19, an urgent problem to solve in 2022 with the emergence of multiple SARS-CoV-2 variants that can escape human immunity elicited by previous infection or vaccination [16,17]. Du et al. and Jin et al. reviewed and estimated the reproduction number of SARS-CoV-2 variants (e.g., Omicron, Delta) to evaluate their transmission advantages [16,18] and found that asymptomatic spreaders could be identified from the transmission network [19]. Mass vaccination, treatment, and mass testing can help reduce the overall population-level attack rate and prove critical to early pandemic mitigation. Wang et al. assessed the epidemiological impact of vaccination on COVID-19 in Hong Kong for the ancestral, Delta, and Omicron strains [20]. The mRNA vaccines are useful for reducing the risk of death for moderate cases but are not significant for severe cases [21]. Koo et al. estimated that fortnightly and weekly mass routine rapid antigen testing would reduce overall infections by 12.8% and 25.2%, respectively [22]. The 2021-22 cross-sectional study using a self-administered questionnaire in Algeria suggested that the impact of preventive measures and vaccination against SARS-CoV-2 is statistically significant in reducing the risk of infection, treatment, and hospitalization [23].
Alongside the studies on COVID-19, several papers also analyzed other pathogens. Zhang et al. tested the structural identifiability of four humidity-driven epidemiology models of influenza transmission [24]. Espitia et al. analyzed the mathematical model of HIV/AIDS presented by Espitia to reveal that reducing homosexual partners can reduce contagion and consequently reach a DFE [25]. Ito et al. and Loi et al. learned about the transmission dynamics of African swine fever in wild boars in South Korea and Italy [26,27]. Soh et al. also proposed a mechanistic model to study the epidemiological impact of fertile Wolbachia-infected female mosquitoes from being released into the environment [28].

Funding

Financial support was provided by the Health and Medical Research Fund, Food and Health Bureau, Government of the Hong Kong Special Administrative Region (grant No. COVID190118), National Natural Science Foundation of China (grant No. 72104208).

Acknowledgments

We acknowledge all authors for their contributions to our Special Issue. We expect that this Special Issue will inspire more studies on the mathematical modeling of infectious disease epidemiology and transmission dynamics. More studies are needed to develop more realistic mechanistic models and better inferential approaches to analyze real-world data.

Conflicts of Interest

The authors declare no conflict of interest.

References

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  2. Du, Z.; Nugent, C.; Galvani, A.P.; Krug, R.M.; Meyers, L.A. Modeling Mitigation of Influenza Epidemics by Baloxavir. Nat. Commun. 2020, 11, 2750. [Google Scholar] [CrossRef] [PubMed]
  3. Chen, Y.; Li, N.; Lourenço, J.; Wang, L.; Cazelles, B.; Dong, L.; Li, B.; Liu, Y.; Jit, M.; Bosse, N.I.; et al. Measuring the Effects of COVID-19-Related Disruption on Dengue Transmission in Southeast Asia and Latin America: A Statistical Modelling Study. Lancet Infect. Dis. 2022, 22, 657–667. [Google Scholar] [CrossRef]
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MDPI and ACS Style

Du, Z.; Luo, W.; Sippy, R.; Wang, L. Editorial: Infectious Disease Epidemiology and Transmission Dynamics. Viruses 2023, 15, 246. https://doi.org/10.3390/v15010246

AMA Style

Du Z, Luo W, Sippy R, Wang L. Editorial: Infectious Disease Epidemiology and Transmission Dynamics. Viruses. 2023; 15(1):246. https://doi.org/10.3390/v15010246

Chicago/Turabian Style

Du, Zhanwei, Wei Luo, Rachel Sippy, and Lin Wang. 2023. "Editorial: Infectious Disease Epidemiology and Transmission Dynamics" Viruses 15, no. 1: 246. https://doi.org/10.3390/v15010246

APA Style

Du, Z., Luo, W., Sippy, R., & Wang, L. (2023). Editorial: Infectious Disease Epidemiology and Transmission Dynamics. Viruses, 15(1), 246. https://doi.org/10.3390/v15010246

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