Pub Date : 2024-08-31DOI: 10.1101/2024.08.30.24312858
Simon K. Camponuri, Alexandra K. Heaney, Gail Sondermeyer Cooksey, Duc J. Vugia, Seema Jain, Daniel L. Swain, John Balmes, Justin V. Remais, Jennifer R Head
Coccidioidomycosis, or Valley fever, is an infectious disease caused by inhalation of Coccidioides spp., fungi found primarily in soils of the southwestern United States. Prior work showed that coccidioidomycosis cases in California sharply increase by nearly 2-fold following wet winters that occur one- and two-years following drought. Statewide drought between 2020-2022 followed by heavy precipitation during the 2022-2023 winter raised concerns over potential increases in coccidioidomycosis cases in the fall of 2023, prompting California Department of Public Health (CDPH) to issue public health alerts. As anticipated, California saw a near record number of cases in 2023, with 9,054 provisional cases reported. During the 2023-2024 California wet season, precipitation was 115% the long-term average, furthering concerns about continued high coccidioidomycosis risk. We developed an ensemble model to forecast coccidioidomycosis cases in California in 2024-2025. Using this model, we predicted a total of 11,846 cases (90% PI: 10,056–14,094) in California between April 1, 2023, and March 31, 2024, encompassing the preliminary state report of 10,593. Our model forecasted 12,244 cases statewide between April 1, 2024, and March 31, 2025 — a 62% increase over the cases reported during the same period two years prior, and on par with the high incidence seen in 2023. The Southern San Joaquin Valley (5,398 cases, 90% PI: 4,556–6,442), Southern Coast (3,322, 90% PI: 2,694–3,961), and Central Coast (1,207 cases, 90% PI: 867–1,585) regions are expected to see the largest number of infections. Our model forecasts that disease incidence will exhibit pronounced seasonality, particularly in endemic regions, with cases rising in June and peaking in November at 1,411 (90% PI: 815–2,172) cases statewide – 98% higher than the peak two years prior (714) and nearly as high as the peak in 2023 (1,462). Near-term forecasts have the potential to inform public health messaging to enhance provider and patient awareness, encourage risk reduction practices, and improve recognition and management of coccidioidomycosis.
{"title":"Recent and forecasted increases in coccidioidomycosis incidence in California linked to hydroclimatic swings","authors":"Simon K. Camponuri, Alexandra K. Heaney, Gail Sondermeyer Cooksey, Duc J. Vugia, Seema Jain, Daniel L. Swain, John Balmes, Justin V. Remais, Jennifer R Head","doi":"10.1101/2024.08.30.24312858","DOIUrl":"https://doi.org/10.1101/2024.08.30.24312858","url":null,"abstract":"Coccidioidomycosis, or Valley fever, is an infectious disease caused by inhalation of <em>Coccidioides</em> spp., fungi found primarily in soils of the southwestern United States. Prior work showed that coccidioidomycosis cases in California sharply increase by nearly 2-fold following wet winters that occur one- and two-years following drought. Statewide drought between 2020-2022 followed by heavy precipitation during the 2022-2023 winter raised concerns over potential increases in coccidioidomycosis cases in the fall of 2023, prompting California Department of Public Health (CDPH) to issue public health alerts. As anticipated, California saw a near record number of cases in 2023, with 9,054 provisional cases reported. During the 2023-2024 California wet season, precipitation was 115% the long-term average, furthering concerns about continued high coccidioidomycosis risk. We developed an ensemble model to forecast coccidioidomycosis cases in California in 2024-2025. Using this model, we predicted a total of 11,846 cases (90% PI: 10,056–14,094) in California between April 1, 2023, and March 31, 2024, encompassing the preliminary state report of 10,593. Our model forecasted 12,244 cases statewide between April 1, 2024, and March 31, 2025 — a 62% increase over the cases reported during the same period two years prior, and on par with the high incidence seen in 2023. The Southern San Joaquin Valley (5,398 cases, 90% PI: 4,556–6,442), Southern Coast (3,322, 90% PI: 2,694–3,961), and Central Coast (1,207 cases, 90% PI: 867–1,585) regions are expected to see the largest number of infections. Our model forecasts that disease incidence will exhibit pronounced seasonality, particularly in endemic regions, with cases rising in June and peaking in November at 1,411 (90% PI: 815–2,172) cases statewide – 98% higher than the peak two years prior (714) and nearly as high as the peak in 2023 (1,462). Near-term forecasts have the potential to inform public health messaging to enhance provider and patient awareness, encourage risk reduction practices, and improve recognition and management of coccidioidomycosis.","PeriodicalId":501071,"journal":{"name":"medRxiv - Epidemiology","volume":"10 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142213453","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-31DOI: 10.1101/2024.08.30.24312842
Sedera Radoniaina Rakotondrasoa, Kadari Cissé, Tieba Millogo, Hajalalaina Rabarisoa, Felix Alain, Seni Kouanda, Julio Rakotonirina
Neonatal mortality remains a major public health challenge, as reductions have stagnated worldwide despite cost-effective interventions in recent years. The temporal evolution of its determinants is insufficiently studied. This study aimed to analyze the dynamics of factors associated with neonatal death in Madagascar between 2003 and 2021.
{"title":"“Dynamics of factors associated with neonatal death in Madagascar: a comparative analysis of the 2003, 2008, 2021 DHS”","authors":"Sedera Radoniaina Rakotondrasoa, Kadari Cissé, Tieba Millogo, Hajalalaina Rabarisoa, Felix Alain, Seni Kouanda, Julio Rakotonirina","doi":"10.1101/2024.08.30.24312842","DOIUrl":"https://doi.org/10.1101/2024.08.30.24312842","url":null,"abstract":"Neonatal mortality remains a major public health challenge, as reductions have stagnated worldwide despite cost-effective interventions in recent years. The temporal evolution of its determinants is insufficiently studied. This study aimed to analyze the dynamics of factors associated with neonatal death in Madagascar between 2003 and 2021.","PeriodicalId":501071,"journal":{"name":"medRxiv - Epidemiology","volume":"109 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142213454","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-31DOI: 10.1101/2024.08.30.24312839
Alicia N.M. Kraay, Mohammad T. Yousafzai, Sonia Qureshi, Jillian Gauld, Farah N. Qamar
Background While trials have demonstrated high efficacy of typhoid conjugate vaccine (TCV), data on effectiveness are limited. We report initial impacts and predict future benefits of TCV from two provinces in Pakistan.
{"title":"Modeling the drivers of differential Typhoid Conjugate Vaccine (TCV) impact in Pakistan: force of infection and age-specific duration of protection","authors":"Alicia N.M. Kraay, Mohammad T. Yousafzai, Sonia Qureshi, Jillian Gauld, Farah N. Qamar","doi":"10.1101/2024.08.30.24312839","DOIUrl":"https://doi.org/10.1101/2024.08.30.24312839","url":null,"abstract":"<strong>Background</strong> While trials have demonstrated high efficacy of typhoid conjugate vaccine (TCV), data on effectiveness are limited. We report initial impacts and predict future benefits of TCV from two provinces in Pakistan.","PeriodicalId":501071,"journal":{"name":"medRxiv - Epidemiology","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142213455","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-31DOI: 10.1101/2024.08.30.24312852
Afia Amoako, Mabel Carabali, Erjia Ge, Ashleigh R Tuite, David N Fisman
The COVID-19 pandemic in Toronto, Canada was unequal for its 2.7 million residents. As a dynamic pandemic, COVID-19 trends might have also varied over space and time. We conducted a spatiotemporal hotspot analysis of COVID-19 over the first four major waves of COVID-19 using three different applications of Moran’s I to highlight the variable experience of COVID-19 infections in Toronto, while describing the potential impact of socioeconomic and sociodemographic factors on increased risk of COVID-19 exposure and infection. Results highlight potential clustering of COVID-19 case rate hot spots in areas with higher concentrations of immigrant and low-income residents and cold spots in areas with more affluent and non-immigrant residents during the first three waves. By the fourth wave, case rate clustering patterns were more dynamic. In all, a better understanding of the unequal COVID-19 pandemic experience in Toronto needs to also consider the dynamic nature of the pandemic.
{"title":"Spatial and Temporal Hotspot Analysis of COVID-19 in Toronto","authors":"Afia Amoako, Mabel Carabali, Erjia Ge, Ashleigh R Tuite, David N Fisman","doi":"10.1101/2024.08.30.24312852","DOIUrl":"https://doi.org/10.1101/2024.08.30.24312852","url":null,"abstract":"The COVID-19 pandemic in Toronto, Canada was unequal for its 2.7 million residents. As a dynamic pandemic, COVID-19 trends might have also varied over space and time. We conducted a spatiotemporal hotspot analysis of COVID-19 over the first four major waves of COVID-19 using three different applications of Moran’s I to highlight the variable experience of COVID-19 infections in Toronto, while describing the potential impact of socioeconomic and sociodemographic factors on increased risk of COVID-19 exposure and infection. Results highlight potential clustering of COVID-19 case rate hot spots in areas with higher concentrations of immigrant and low-income residents and cold spots in areas with more affluent and non-immigrant residents during the first three waves. By the fourth wave, case rate clustering patterns were more dynamic. In all, a better understanding of the unequal COVID-19 pandemic experience in Toronto needs to also consider the dynamic nature of the pandemic.","PeriodicalId":501071,"journal":{"name":"medRxiv - Epidemiology","volume":"4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142226836","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-31DOI: 10.1101/2024.08.30.24312846
Shazelin Alipitchay, Muhammad Aswad Alias, Sharifah Nur Shahirah Syed Abdul Hamid, Rabizah Hamzah, Norain Mansor, Nurulhusna Ab. Hamid, Hidayatulfathi Othman
The interaction between dengue cases, entomological and meteorological variables has remained intricate for decades. Validated facts are important to form robust decision making with the adoption of safer and sustainable efforts. This study aims to elucidate the relationship between the variables in the long run and short-term dynamic focusing in Melaka, Malaysia, in an attempt to improve the understanding of the variables and their temporal associations. This study quantifies the variables on their temporal associations, potential time lags, and dynamic interplays between all the variable data sets. The research applies a Johansen Cointegration Test and Vector Error Correction Model to validate long term run and examine short-term deviations among dengue cases, temperature, ovitrap and sticky ovitrap data from 2020-2022. Empirical findings prove that temperature, sticky ovitrap index (SOI) and ovitrap index (OI) has a significant and unique long-run equilibrium relationship with dengue cases. The short-term equilibrium results display a robust causality between variables. The model fit elucidates 74.2% of the dynamics. The VECM model provides an excellent trade-off between goodness of fit and complexity in describing the variables examined. Previous dengue occurrences predicted a surge of new dengue cases while preserving the cyclical pattern. The model predicts the utility and efficacy of sticky ovitraps. It also validates ovitrap use as a surveillance tool and offers substantiation of the influence of temperature on the progression of dengue cases.
{"title":"Temporal and interaction dynamics of dengue cases, entomological and meteorological variables in Melaka, Malaysia: A multivariate time series analysis","authors":"Shazelin Alipitchay, Muhammad Aswad Alias, Sharifah Nur Shahirah Syed Abdul Hamid, Rabizah Hamzah, Norain Mansor, Nurulhusna Ab. Hamid, Hidayatulfathi Othman","doi":"10.1101/2024.08.30.24312846","DOIUrl":"https://doi.org/10.1101/2024.08.30.24312846","url":null,"abstract":"The interaction between dengue cases, entomological and meteorological variables has remained intricate for decades. Validated facts are important to form robust decision making with the adoption of safer and sustainable efforts. This study aims to elucidate the relationship between the variables in the long run and short-term dynamic focusing in Melaka, Malaysia, in an attempt to improve the understanding of the variables and their temporal associations. This study quantifies the variables on their temporal associations, potential time lags, and dynamic interplays between all the variable data sets. The research applies a Johansen Cointegration Test and Vector Error Correction Model to validate long term run and examine short-term deviations among dengue cases, temperature, ovitrap and sticky ovitrap data from 2020-2022. Empirical findings prove that temperature, sticky ovitrap index (SOI) and ovitrap index (OI) has a significant and unique long-run equilibrium relationship with dengue cases. The short-term equilibrium results display a robust causality between variables. The model fit elucidates 74.2% of the dynamics. The VECM model provides an excellent trade-off between goodness of fit and complexity in describing the variables examined. Previous dengue occurrences predicted a surge of new dengue cases while preserving the cyclical pattern. The model predicts the utility and efficacy of sticky ovitraps. It also validates ovitrap use as a surveillance tool and offers substantiation of the influence of temperature on the progression of dengue cases.","PeriodicalId":501071,"journal":{"name":"medRxiv - Epidemiology","volume":"26 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142226839","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-29DOI: 10.1101/2024.08.28.24312695
Marta Pineda-Moncusí, Alexandros Rekkas, Álvaro Martínez Pérez, Angela Leis, Carlos Lopez Gomez, Erwin Bruninx, Filip Maljković, Jordi Rodeiro, Michael Franz, Miguel-Angel Mayer, Neva Eleangovan, Pantelis Natsiavas, Selçuk Şen, Steven Cooper, Sulev Reisberg, Katrin Manlik, Francisco Sánchez-Sáez, Beatriz del Pino, Albert Prats Uribe, Ali Yağız Üresin, Ana Danilović Bastić, Ana Maria Rodrigues, Anna Palomar-Cros, Annelies Verbiest, Barış Erdoğan, Carina Dinkel- Keuthage, Carmen Olga Torre, Caroline de Beukelaar, Caroline Eteve-Pitsaer, Cátia F. Gonçalves, Costantino de Palma, Cristina Gavina, Daniel Dedman, David Brendan Price, Denisa Gabriela Balan, Dirk Enders, Elisa Henke, Elyne Scheurwegs, Emma Callewaert, Encarnación Pérez Martínez, Eng Hooi Tan, Eric Fey, Fabian Prasser, Frank Staelens, Fredrik Nyberg, Gianmario Candore, Gianny Mestdach, Hadas Shachaf, Huiqi Li, Ines Reinecke, Irene López-Sánchez, Javier de la Cruz Bertolo, Jelle Evers, João Firmino-Machado, Jonas Wastesson, Juan Luis Cruz Bermúdez, Juan Manuel Ramírez-Anguita, Kimmo Porkka, Kristina Johnell, Lieselot Cool, Loretta Zsuzsa Kiss, Luca Moscetti, Manon Merkelbach, Mariana Canelas-Pais, Massimo Dominici, Máté Szilcz, Matteo Puntoni, Mees Mosseveld, Mina Tadrous, Mona Bové, Nadav Rappoport, Noelia García Barrio, Otto Ettala, Paolo Baili, Pau Pericàs Pulido, Paula Rubio Mayo, Peter Prinsen, Raeleesha Norris, Ravinder Claire, Roberto Lillini, Silvia Lazzarelli, Talita Duarte-Salles, Tiago Taveira-Gomes, Tim Jansen, Ulrich Keilholz, Xintong Li, Daniel Prieto-Alhambra, Peter R. Rijnbeek, Theresa Burkard
Importance Drug production not meeting the demand leaves affected patients in a vulnerable position.
重要性 药品生产不能满足需求，使受影响的病人处于弱势地位。
{"title":"Trends of drug use with suggested shortages and their alternatives across 41 real world data sources and 18 countries in Europe and North America","authors":"Marta Pineda-Moncusí, Alexandros Rekkas, Álvaro Martínez Pérez, Angela Leis, Carlos Lopez Gomez, Erwin Bruninx, Filip Maljković, Jordi Rodeiro, Michael Franz, Miguel-Angel Mayer, Neva Eleangovan, Pantelis Natsiavas, Selçuk Şen, Steven Cooper, Sulev Reisberg, Katrin Manlik, Francisco Sánchez-Sáez, Beatriz del Pino, Albert Prats Uribe, Ali Yağız Üresin, Ana Danilović Bastić, Ana Maria Rodrigues, Anna Palomar-Cros, Annelies Verbiest, Barış Erdoğan, Carina Dinkel- Keuthage, Carmen Olga Torre, Caroline de Beukelaar, Caroline Eteve-Pitsaer, Cátia F. Gonçalves, Costantino de Palma, Cristina Gavina, Daniel Dedman, David Brendan Price, Denisa Gabriela Balan, Dirk Enders, Elisa Henke, Elyne Scheurwegs, Emma Callewaert, Encarnación Pérez Martínez, Eng Hooi Tan, Eric Fey, Fabian Prasser, Frank Staelens, Fredrik Nyberg, Gianmario Candore, Gianny Mestdach, Hadas Shachaf, Huiqi Li, Ines Reinecke, Irene López-Sánchez, Javier de la Cruz Bertolo, Jelle Evers, João Firmino-Machado, Jonas Wastesson, Juan Luis Cruz Bermúdez, Juan Manuel Ramírez-Anguita, Kimmo Porkka, Kristina Johnell, Lieselot Cool, Loretta Zsuzsa Kiss, Luca Moscetti, Manon Merkelbach, Mariana Canelas-Pais, Massimo Dominici, Máté Szilcz, Matteo Puntoni, Mees Mosseveld, Mina Tadrous, Mona Bové, Nadav Rappoport, Noelia García Barrio, Otto Ettala, Paolo Baili, Pau Pericàs Pulido, Paula Rubio Mayo, Peter Prinsen, Raeleesha Norris, Ravinder Claire, Roberto Lillini, Silvia Lazzarelli, Talita Duarte-Salles, Tiago Taveira-Gomes, Tim Jansen, Ulrich Keilholz, Xintong Li, Daniel Prieto-Alhambra, Peter R. Rijnbeek, Theresa Burkard","doi":"10.1101/2024.08.28.24312695","DOIUrl":"https://doi.org/10.1101/2024.08.28.24312695","url":null,"abstract":"<strong>Importance</strong> Drug production not meeting the demand leaves affected patients in a vulnerable position.","PeriodicalId":501071,"journal":{"name":"medRxiv - Epidemiology","volume":"59 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142213460","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-29DOI: 10.1101/2024.08.29.24312767
Ilse Westerhof, Reina Sikkema, Ganna Rozhnova, Janko van Beek, Marion Koopmans, Patricia Bruijning-Verhagen
Background/Rationale We investigated the effect of pre-existing antibodies against SARS-CoV-2 and seasonal human coronaviruses on infection outcomes in Omicron BA1/2 exposed household members from January to March 2022.
{"title":"The effect of pre-existing coronavirus antibodies on SARS-CoV-2 infection outcomes in exposed household members","authors":"Ilse Westerhof, Reina Sikkema, Ganna Rozhnova, Janko van Beek, Marion Koopmans, Patricia Bruijning-Verhagen","doi":"10.1101/2024.08.29.24312767","DOIUrl":"https://doi.org/10.1101/2024.08.29.24312767","url":null,"abstract":"<strong>Background/Rationale</strong> We investigated the effect of pre-existing antibodies against SARS-CoV-2 and seasonal human coronaviruses on infection outcomes in Omicron BA1/2 exposed household members from January to March 2022.","PeriodicalId":501071,"journal":{"name":"medRxiv - Epidemiology","volume":"275 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142213458","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-29DOI: 10.1101/2024.08.29.24312763
Liangying Yin, Menghui Liu, Yujia Shi, Jinghong Qiu, Hon-cheong So
Accurate identification of direct causal(parental) variables for a target is of primary interest in many applications, especially in biomedicine. It could promote our understanding of the underlying pathophysiological mechanism and facilitate the discovery of new biomarkers and therapeutic targets for studied clinical outcomes. However, many researchers are inclined to resort to association-based machine learning methods to identify outcome-associated variables. And many of the identified variables may prove to be irrelevant. On the other hand, there is a lack of an efficient method for reliable parental set identification, especially in high-dimensional settings (e.g., biomedicine).
{"title":"Direct causal variable discovery leveraging the invariance principle: application in biomedical studies","authors":"Liangying Yin, Menghui Liu, Yujia Shi, Jinghong Qiu, Hon-cheong So","doi":"10.1101/2024.08.29.24312763","DOIUrl":"https://doi.org/10.1101/2024.08.29.24312763","url":null,"abstract":"Accurate identification of direct causal(parental) variables for a target is of primary interest in many applications, especially in biomedicine. It could promote our understanding of the underlying pathophysiological mechanism and facilitate the discovery of new biomarkers and therapeutic targets for studied clinical outcomes. However, many researchers are inclined to resort to association-based machine learning methods to identify outcome-associated variables. And many of the identified variables may prove to be irrelevant. On the other hand, there is a lack of an efficient method for reliable parental set identification, especially in high-dimensional settings (e.g., biomedicine).","PeriodicalId":501071,"journal":{"name":"medRxiv - Epidemiology","volume":"27 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142213456","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-29DOI: 10.1101/2024.08.28.24312512
Sharon K. Greene, Julia Latash, Eric R. Peterson, Alison Levin-Rector, Elizabeth Luoma, Jade C. Wang, Kevin Bernard, Aaron Olsen, Lan Li, HaeNa Waechter, Aria Mattias, Rebecca Rohrer, Martin Kulldorff
Genomic surveillance data are used to detect communicable disease clusters, typically by applying rule-based signaling criteria, which can be arbitrary. We applied the prospective tree-temporal scan statistic (TreeScan) to genomic data with a hierarchical nomenclature to search for recent case increases at any granularity, from large phylogenetic branches to small groups of indistinguishable isolates. Using COVID-19 and salmonellosis cases diagnosed among New York City (NYC) residents and reported to the NYC Health Department, we conducted weekly analyses to detect emerging SARS-CoV-2 variants based on Pango lineages and clusters of Salmonella isolates based on allele codes. The SARS-CoV-2 Omicron subvariant EG.5.1 first signaled as locally emerging on June 22, 2023, seven weeks before the World Health Organization designated it as a variant of interest. During one year of salmonellosis analyses, TreeScan detected fifteen credible clusters worth investigating for common exposures and two data quality issues for correction. A challenge was maintaining timely and specific lineage assignments, and a limitation was that genetic distances between tree nodes were not considered. By automatically sifting through genomic data and generating ranked shortlists of nodes with statistically unusual recent case increases, TreeScan assisted in detecting emerging communicable disease clusters and in prioritizing them for investigation.
{"title":"Applying Prospective Tree-Temporal Scan Statistics to Genomic Surveillance Data to Detect Emerging SARS-CoV-2 Variants and Salmonellosis Clusters in New York City","authors":"Sharon K. Greene, Julia Latash, Eric R. Peterson, Alison Levin-Rector, Elizabeth Luoma, Jade C. Wang, Kevin Bernard, Aaron Olsen, Lan Li, HaeNa Waechter, Aria Mattias, Rebecca Rohrer, Martin Kulldorff","doi":"10.1101/2024.08.28.24312512","DOIUrl":"https://doi.org/10.1101/2024.08.28.24312512","url":null,"abstract":"Genomic surveillance data are used to detect communicable disease clusters, typically by applying rule-based signaling criteria, which can be arbitrary. We applied the prospective tree-temporal scan statistic (TreeScan) to genomic data with a hierarchical nomenclature to search for recent case increases at any granularity, from large phylogenetic branches to small groups of indistinguishable isolates. Using COVID-19 and salmonellosis cases diagnosed among New York City (NYC) residents and reported to the NYC Health Department, we conducted weekly analyses to detect emerging SARS-CoV-2 variants based on Pango lineages and clusters of <em>Salmonella</em> isolates based on allele codes. The SARS-CoV-2 Omicron subvariant EG.5.1 first signaled as locally emerging on June 22, 2023, seven weeks before the World Health Organization designated it as a variant of interest. During one year of salmonellosis analyses, TreeScan detected fifteen credible clusters worth investigating for common exposures and two data quality issues for correction. A challenge was maintaining timely and specific lineage assignments, and a limitation was that genetic distances between tree nodes were not considered. By automatically sifting through genomic data and generating ranked shortlists of nodes with statistically unusual recent case increases, TreeScan assisted in detecting emerging communicable disease clusters and in prioritizing them for investigation.","PeriodicalId":501071,"journal":{"name":"medRxiv - Epidemiology","volume":"103 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142213459","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-08DOI: 10.1101/2024.08.07.24311639
Gabrielle Jayme, Ju-Ling Liu, Jose Hector Galvez, Sarah Julia Reiling, Sukriye Celikkol Aydin, Arnaud N'Guessan, Sally Lee, Shu-Huang Chen, Alexandra Tsitouras, Fernando Sanchez-Quete, Thomas Maere, Eyerusalem Goitom, Mounia Hachad, Elisabeth Mercier, Stephanie Katharine Loeb, Peter Vanrolleghem, Sarah Dorner, Robert Delatolla, B. Jesse Shapiro, Dominic Frigon, Jiannis Ragoussis, Terrance P. Snutch
During the COVID-19 pandemic, the monitoring of SARS-COV-2 RNA in wastewater was used to track the evolution and emergence of variant lineages and gauge infection levels in the community, informing appropriate public health responses without relying solely on clinical testing. As more sublineages were discovered, it increased the difficulty in identifying distinct variants in a mixed population sample, particularly those without a known lineage. Here, we compare two next-generation sequencing technologies, Illumina and Nanopore, in order to determine their efficacy at detecting variants of differing abundance, using 248 wastewater samples from various Quebec and Ontario cities. Our study used two analytical approaches to identify main variants in the samples: the presence of signature and marker mutations, and the co-occurrence of signature mutations within the same amplicon. We observed that each sequencing method detected certain variants at different frequencies as each method preferentially detects mutations of distinct variants. Illumina sequencing detected more mutations with a predominant lineage that is in low abundance across the population or unknown for that time period, while Nanopore sequencing had a higher detection rate of mutations that are predominantly found in the high abundance B.1.1.7 (Alpha) lineage as well as a higher sequencing rate of co-occurring mutations in the same amplicon. We present a workflow that integrates short read and long read sequencing to improve the detection of SARS-CoV-2 variant lineages in mixed population samples, such as wastewater.
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