Mathematical models of disease transmission have contributed significantly in effective decision making to the public health authorities from the onset of the current COVID-19 pandemic. Population-level modeling helped us to better understand the consequences of non-pharmaceutical interventions and allowed us to even assess counter-factual outcomes. Behavioral measures, such as increased physical distancing, were strictly maintained in order to contain the pandemic SARS-CoV-2 spread during the past three years, that in fact led to decrease in most of the other circulating respiratory viruses like respiratory syncytial virus (RSV), seasonal strains of influenza virus, rhinovirus or common cold, para-influenza virus, enterovirus as well as bacterial pathogens. Now that those behavioral measures have been removed or somewhat relaxed, the respiratory pathogens are hitting back and mainly striking the children, who have been shielded from them during the pandemic and/or have the opportunity to build up the immunity that might have dampened the severity of their first infections. As a result, the human population now has more than one circulating pathogens including the emerging variants of SARS-CoV-2 with high pandemic potential who are not only temporally coincidental but also share same transmission routes and target cells. In addition, many experimental studies have identified heterogeneous disease outcomes for coinfection with more than one respiratory pathogen, where it was mostly dependent on the coinfection pair, order, and/or timing of the secondary infection. Therefore, it is critical to understand the potential interactions among them, and consequences of getting simultaneous and subsequent multi-pathogen infections in order to be prepared for the next pandemic as well as the emerging situation with non-SARS-CoV-2 viral spread. Moreover, since vaccines are not available for most of these circulating pathogens, it is paramount to know how vaccination against one virus is going to impact on people who are infected by another.
In this Special Issue, we will focus on the recent developments of mathematical models of population-level transmission models, network models, and artificial intelligence/machine learning tools for pandemic pathogens, potential interactions of pandemic strain with seasonal pathogens, two or more concurrent and subsequent epidemics, control strategies like vaccinations, and application of artificial intelligence/machine learning in epidemiology as a scope. We will also ask for modeling human behavior toward control measures and their effect on the spread of single and multi-pathogens.
We request prospective authors to submit original research articles, review, methodologies, short communications, opinions, mainly dealing with, but not limited to, the following topics: disease transmission dynamics, behavioral modeling in disease transmission, single or multi-strain emerging infectious diseases, homophily on the contact network, pandemic control measures, application of AI/ML in epidemiology.
All articles will be comprised of multidisciplinary approaches to handle the existing challenges faced in this triple-pandemic like emerging situation. The vision of this special issue is to bring clinicians, engineerings, basic scientists, physicists and mathematicians, and big data analytics together to provide the readers current state of the art of quantitative approach in understanding control against the circulating infectious pathogens, and aiding development of novel intervention approaches, and awareness.
Mathematical models of disease transmission have contributed significantly in effective decision making to the public health authorities from the onset of the current COVID-19 pandemic. Population-level modeling helped us to better understand the consequences of non-pharmaceutical interventions and allowed us to even assess counter-factual outcomes. Behavioral measures, such as increased physical distancing, were strictly maintained in order to contain the pandemic SARS-CoV-2 spread during the past three years, that in fact led to decrease in most of the other circulating respiratory viruses like respiratory syncytial virus (RSV), seasonal strains of influenza virus, rhinovirus or common cold, para-influenza virus, enterovirus as well as bacterial pathogens. Now that those behavioral measures have been removed or somewhat relaxed, the respiratory pathogens are hitting back and mainly striking the children, who have been shielded from them during the pandemic and/or have the opportunity to build up the immunity that might have dampened the severity of their first infections. As a result, the human population now has more than one circulating pathogens including the emerging variants of SARS-CoV-2 with high pandemic potential who are not only temporally coincidental but also share same transmission routes and target cells. In addition, many experimental studies have identified heterogeneous disease outcomes for coinfection with more than one respiratory pathogen, where it was mostly dependent on the coinfection pair, order, and/or timing of the secondary infection. Therefore, it is critical to understand the potential interactions among them, and consequences of getting simultaneous and subsequent multi-pathogen infections in order to be prepared for the next pandemic as well as the emerging situation with non-SARS-CoV-2 viral spread. Moreover, since vaccines are not available for most of these circulating pathogens, it is paramount to know how vaccination against one virus is going to impact on people who are infected by another.
In this Special Issue, we will focus on the recent developments of mathematical models of population-level transmission models, network models, and artificial intelligence/machine learning tools for pandemic pathogens, potential interactions of pandemic strain with seasonal pathogens, two or more concurrent and subsequent epidemics, control strategies like vaccinations, and application of artificial intelligence/machine learning in epidemiology as a scope. We will also ask for modeling human behavior toward control measures and their effect on the spread of single and multi-pathogens.
We request prospective authors to submit original research articles, review, methodologies, short communications, opinions, mainly dealing with, but not limited to, the following topics: disease transmission dynamics, behavioral modeling in disease transmission, single or multi-strain emerging infectious diseases, homophily on the contact network, pandemic control measures, application of AI/ML in epidemiology.
All articles will be comprised of multidisciplinary approaches to handle the existing challenges faced in this triple-pandemic like emerging situation. The vision of this special issue is to bring clinicians, engineerings, basic scientists, physicists and mathematicians, and big data analytics together to provide the readers current state of the art of quantitative approach in understanding control against the circulating infectious pathogens, and aiding development of novel intervention approaches, and awareness.
