Kelsey Shaw, Jennifer Peterson, Neda Jalali, Saikanth Ratnavale, Manar Alkuzweny, Carly Barbera, Alan Costello, Liam Emerick, Guido Espana, Alexander Meyer, Stacy Mowry, Marya Poterek, Carol de Souza Moreira, Eric Morgan, Sean M Moore, Alex Perkins
{"title":"人类共循环病原体:机理传播模型的系统回顾","authors":"Kelsey Shaw, Jennifer Peterson, Neda Jalali, Saikanth Ratnavale, Manar Alkuzweny, Carly Barbera, Alan Costello, Liam Emerick, Guido Espana, Alexander Meyer, Stacy Mowry, Marya Poterek, Carol de Souza Moreira, Eric Morgan, Sean M Moore, Alex Perkins","doi":"10.1101/2024.09.16.24313749","DOIUrl":null,"url":null,"abstract":"Historically, most mathematical models of infectious disease dynamics have focused on a single pathogen, despite the ubiquity of co-circulating pathogens in the real world. We conducted a systematic review of 311 published papers that included a mechanistic, population-level model of co-circulating human pathogens. We identified the types of pathogens represented in this literature, techniques used, and motivations for conducting these studies. We also created a complexity index to quantify the degree to which co-circulating pathogen models diverged from single-pathogen models. We found that the emergence of new pathogens, such as HIV and SARS-CoV-2, precipitated modeling activity of the emerging pathogen with established pathogens. Pathogen characteristics also tended to drive modeling activity; for example, HIV suppresses the immune response, eliciting interesting dynamics when it is modeled with other pathogens. The motivations driving these studies were varied but could be divided into two major categories: exploration of dynamics and evaluation of interventions. Finally, we found that model complexity quickly increases as additional pathogens are added. Future potential avenues of research we identified include investigating the effects of misdiagnosis of clinically similar co-circulating pathogens and characterizing the impacts of one pathogen on public health resources available to curtail the spread of other pathogens.","PeriodicalId":501509,"journal":{"name":"medRxiv - Infectious Diseases","volume":"50 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Co-circulating pathogens of humans: A systematic review of mechanistic transmission models\",\"authors\":\"Kelsey Shaw, Jennifer Peterson, Neda Jalali, Saikanth Ratnavale, Manar Alkuzweny, Carly Barbera, Alan Costello, Liam Emerick, Guido Espana, Alexander Meyer, Stacy Mowry, Marya Poterek, Carol de Souza Moreira, Eric Morgan, Sean M Moore, Alex Perkins\",\"doi\":\"10.1101/2024.09.16.24313749\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Historically, most mathematical models of infectious disease dynamics have focused on a single pathogen, despite the ubiquity of co-circulating pathogens in the real world. We conducted a systematic review of 311 published papers that included a mechanistic, population-level model of co-circulating human pathogens. We identified the types of pathogens represented in this literature, techniques used, and motivations for conducting these studies. We also created a complexity index to quantify the degree to which co-circulating pathogen models diverged from single-pathogen models. We found that the emergence of new pathogens, such as HIV and SARS-CoV-2, precipitated modeling activity of the emerging pathogen with established pathogens. Pathogen characteristics also tended to drive modeling activity; for example, HIV suppresses the immune response, eliciting interesting dynamics when it is modeled with other pathogens. The motivations driving these studies were varied but could be divided into two major categories: exploration of dynamics and evaluation of interventions. Finally, we found that model complexity quickly increases as additional pathogens are added. Future potential avenues of research we identified include investigating the effects of misdiagnosis of clinically similar co-circulating pathogens and characterizing the impacts of one pathogen on public health resources available to curtail the spread of other pathogens.\",\"PeriodicalId\":501509,\"journal\":{\"name\":\"medRxiv - Infectious Diseases\",\"volume\":\"50 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-09-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"medRxiv - Infectious Diseases\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1101/2024.09.16.24313749\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"medRxiv - Infectious Diseases","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1101/2024.09.16.24313749","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Co-circulating pathogens of humans: A systematic review of mechanistic transmission models
Historically, most mathematical models of infectious disease dynamics have focused on a single pathogen, despite the ubiquity of co-circulating pathogens in the real world. We conducted a systematic review of 311 published papers that included a mechanistic, population-level model of co-circulating human pathogens. We identified the types of pathogens represented in this literature, techniques used, and motivations for conducting these studies. We also created a complexity index to quantify the degree to which co-circulating pathogen models diverged from single-pathogen models. We found that the emergence of new pathogens, such as HIV and SARS-CoV-2, precipitated modeling activity of the emerging pathogen with established pathogens. Pathogen characteristics also tended to drive modeling activity; for example, HIV suppresses the immune response, eliciting interesting dynamics when it is modeled with other pathogens. The motivations driving these studies were varied but could be divided into two major categories: exploration of dynamics and evaluation of interventions. Finally, we found that model complexity quickly increases as additional pathogens are added. Future potential avenues of research we identified include investigating the effects of misdiagnosis of clinically similar co-circulating pathogens and characterizing the impacts of one pathogen on public health resources available to curtail the spread of other pathogens.