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Latif MT, Purhanudin N, Afandi NZM, Cambaliza MOL, Halim NDA, Hawari NSSL, Hien TT, Hlaing OMT, Jansz WRLH, Khokhar MF, Lestari P, Lung SCC, Naja M, Oanh NTK, Othman M, Salam A, Salim PM, Song CK, Fujinawa T, Tanimoto H, Yu LE, Crawford JH. In-depth analysis of ambient air pollution changes due to the COVID-19 pandemic in the Asian Monsoon region. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 941:173145. [PMID: 38768732 DOI: 10.1016/j.scitotenv.2024.173145] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/04/2023] [Revised: 05/08/2024] [Accepted: 05/09/2024] [Indexed: 05/22/2024]
Abstract
The COVID-19 pandemic has given a chance for researchers and policymakers all over the world to study the impact of lockdowns on air quality in each country. This review aims to investigate the impact of the restriction of activities during the lockdowns in the Asian Monsoon region on the main criteria air pollutants. The various types of lockdowns implemented in each country were based on the severity of the COVID-19 pandemic. The concentrations of major air pollutants, especially particulate matter (PM) and nitrogen dioxide (NO2), reduced significantly in all countries, especially in South Asia (India and Bangladesh), during periods of full lockdown. There were also indications of a significant reduction of sulfur dioxide (SO2) and carbon monoxide (CO). At the same time, there were indications of increasing trends in surface ozone (O3), presumably due to nonlinear chemistry associated with the reduction of oxides of nitrogens (NOX). The reduction in the concentration of air pollutants can also be seen in satellite images. The results of aerosol optical depth (AOD) values followed the PM concentrations in many cities. A significant reduction of NO2 was recorded by satellite images in almost all cities in the Asian Monsoon region. The major reductions in air pollutants were associated with reductions in mobility. Pakistan, Bangladesh, Myanmar, Vietnam, and Taiwan had comparatively positive gross domestic product growth indices in comparison to other Asian Monsoon nations during the COVID-19 pandemic. A positive outcome suggests that the economy of these nations, particularly in terms of industrial activity, persisted during the COVID-19 pandemic. Overall, the lockdowns implemented during COVID-19 suggest that air quality in the Asian Monsoon region can be improved by the reduction of emissions, especially those due to mobility as an indicator of traffic in major cities.
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Affiliation(s)
- Mohd Talib Latif
- Department of Earth Sciences and Environment, Universiti Kebangsaan Malaysia, 43600 Bangi, Selangor, Malaysia.
| | - Noorain Purhanudin
- Department of Earth Sciences and Environment, Universiti Kebangsaan Malaysia, 43600 Bangi, Selangor, Malaysia
| | - Nur Zulaikha Mohd Afandi
- Department of Earth Sciences and Environment, Universiti Kebangsaan Malaysia, 43600 Bangi, Selangor, Malaysia; East Coast Environmental Research Institute (ESERI), Universiti Sultan Zainal Abidin, 21300 Kuala Nerus, Terengganu, Malaysia
| | - Maria Obiminda L Cambaliza
- Department of Physics, Ateneo de Manila University, Air Quality Dynamics Laboratory, Manila Observatory, Katipunan Ave., Quezon City, Metro Manila 1101, Philippines
| | - Nor Diana Abdul Halim
- Department of Earth Sciences and Environment, Universiti Kebangsaan Malaysia, 43600 Bangi, Selangor, Malaysia; Faculty of Applied Sciences, Universiti Teknologi MARA (UiTM), Sarawak Branch, Samarahan 2, 94300 Kota Samarahan, Sarawak, Malaysia
| | | | - To Thi Hien
- Faculty of Environment, University of Science, Vietnam National University Ho Chi Minh City, Ho Chi Minh City 700000, Viet Nam
| | | | | | - Muhammad Fahim Khokhar
- Institute of Environmental Sciences and Engineering, National University of Sciences and Technology (NUST), Islamabad 44000, Pakistan
| | - Puji Lestari
- Faculty of Civil and Environmental Engineering, Bandung Institute of Technology, Ganesha 10, Bandung, 40132, Indonesia
| | | | - Manish Naja
- Aryabhatta Research Institute of Observational Sciences, Manora Peak, Nainital, Uttarakhand 263129, India
| | - Nguyen Thi Kim Oanh
- Environmental Engineering and Management, Asian Institute of Technology, Pathumthani 12120, Thailand
| | - Murnira Othman
- Institute for Environment and Development (LESTARI), Universiti Kebangsaan Malaysia, 43600 Bangi, Selangor, Malaysia
| | - Abdus Salam
- Department of Chemistry, Faculty of Science, University of Dhaka, Dhaka 100, Bangladesh
| | - Pauziyah Mohammad Salim
- Department of Earth Sciences and Environment, Universiti Kebangsaan Malaysia, 43600 Bangi, Selangor, Malaysia; School of Geomatic Science and Natural Resources, College of Built Environment (CBE), Universiti Teknologi MARA, 40450 Shah Alam, Selangor, Malaysia
| | - Chang-Keun Song
- Department of Urban & Environmental Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan, Republic of Korea
| | - Tamaki Fujinawa
- National Institute for Environmental Studies, 16-2 Onogawa, Tsukuba, Ibaraki 305-8506, Japan
| | - Hiroshi Tanimoto
- National Institute for Environmental Studies, 16-2 Onogawa, Tsukuba, Ibaraki 305-8506, Japan
| | - Liya E Yu
- Department of Civil and Environmental Engineering, National University of Singapore, Singapore 117576, Singapore
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Bender RG, Sirota SB, Swetschinski LR, Dominguez RMV, Novotney A, Wool EE, Ikuta KS, Vongpradith A, Rogowski ELB, Doxey M, Troeger CE, Albertson SB, Ma J, He J, Maass KL, A.F.Simões E, Abdoun M, Abdul Aziz JM, Abdulah DM, Abu Rumeileh S, Abualruz H, Aburuz S, Adepoju AV, Adha R, Adikusuma W, Adra S, Afraz A, Aghamiri S, Agodi A, Ahmadzade AM, Ahmed H, Ahmed A, Akinosoglou K, AL-Ahdal TMA, Al-amer RM, Albashtawy M, AlBataineh MT, Alemi H, Al-Gheethi AAS, Ali A, Ali SSS, Alqahtani JS, AlQudah M, Al-Tawfiq JA, Al-Worafi YM, Alzoubi KH, Amani R, Amegbor PM, Ameyaw EK, Amuasi JH, Anil A, Anyanwu PE, Arafat M, Areda D, Arefnezhad R, Atalell KA, Ayele F, Azzam AY, Babamohamadi H, Babin FX, Bahurupi Y, Baker S, Banik B, Barchitta M, Barqawi HJ, Basharat Z, Baskaran P, Batra K, Batra R, Bayileyegn NS, Beloukas A, Berkley JA, Beyene KA, Bhargava A, Bhattacharjee P, Bielicki JA, Bilalaga MM, Bitra VR, Brown CS, Burkart K, Bustanji Y, Carr S, Chahine Y, Chattu VK, Chichagi F, Chopra H, Chukwu IS, Chung E, Dadana S, Dai X, Dandona L, Dandona R, Darban I, Dash NR, Dashti M, Dashtkoohi M, Dekker DM, Delgado-Enciso I, Devanbu VGC, Dhama K, Diao N, Do THP, Dokova KG, Dolecek C, Dziedzic AM, Eckmanns T, Ed-Dra A, Efendi F, Eftekharimehrabad A, Eyre DW, Fahim A, Feizkhah A, Felton TW, Ferreira N, Flor LS, Gaihre S, Gebregergis MW, Gebrehiwot M, Geffers C, Gerema U, Ghaffari K, Goldust M, Goleij P, Guan SY, Gudeta MD, Guo C, Gupta VB, Gupta I, Habibzadeh F, Hadi NR, Haeuser E, Hailu WB, Hajibeygi R, Haj-Mirzaian A, Haller S, Hamiduzzaman M, Hanifi N, Hansel J, Hasnain MS, Haubold J, Hoan NQ, Huynh HH, Iregbu KC, Islam MR, Jafarzadeh A, Jairoun AA, Jalili M, Jomehzadeh N, Joshua CE, Kabir MA, Kamal Z, Kanmodi KK, Kantar RS, Karimi Behnagh A, Kaur N, Kaur H, Khamesipour F, Khan MN, Khan suheb MZ, Khanal V, Khatab K, Khatib MN, Kim G, Kim K, Kitila ATT, Komaki S, Krishan K, Krumkamp R, Kuddus MA, Kurniasari MD, Lahariya C, Latifinaibin K, Le NHH, Le TTT, Le TDT, Lee SW, LEPAPE A, Lerango TL, Li MC, Mahboobipour AA, Malhotra K, Mallhi TH, Manoharan A, Martinez-Guerra BA, Mathioudakis AG, Mattiello R, May J, McManigal B, McPhail SM, Mekene Meto T, Mendez-Lopez MAM, Meo SA, Merati M, Mestrovic T, Mhlanga L, Minh LHN, Misganaw A, Mishra V, Misra AK, Mohamed NS, Mohammadi E, Mohammed M, Mohammed M, Mokdad AH, Monasta L, Moore CE, Motappa R, Mougin V, Mousavi P, Mulita F, Mulu AA, Naghavi P, Naik GR, Nainu F, Nair TS, Nargus S, Negaresh M, Nguyen HTH, Nguyen DH, Nguyen VT, Nikolouzakis TK, Noman EA, Nri-Ezedi CA, Odetokun IA, Okwute PG, Olana MD, Olanipekun TO, Olasupo OO, Olivas-Martinez A, Ordak M, Ortiz-Brizuela E, Ouyahia A, Padubidri JR, Pak A, Pandey A, Pantazopoulos I, Parija PP, Parikh RR, Park S, Parthasarathi A, Pashaei A, Peprah P, Pham HT, Poddighe D, Pollard A, Ponce-De-Leon A, Prakash PY, Prates EJS, Quan NK, Raee P, Rahim F, Rahman M, Rahmati M, Ramasamy SK, Ranjan S, Rao IR, Rashid AM, Rattanavong S, Ravikumar N, Reddy MMRK, Redwan EMM, Reiner RC, Reyes LF, Roberts T, Rodrigues M, Rosenthal VD, Roy P, Runghien T, Saeed U, Saghazadeh A, Saheb Sharif-Askari N, Saheb Sharif-Askari F, Sahoo SS, Sahu M, Sakshaug JW, Salami AA, Saleh MA, Salehi omran H, Sallam M, Samadzadeh S, Samodra YL, Sanjeev RK, Sarasmita MA, Saravanan A, Sartorius B, Saulam J, Schumacher AE, Seyedi SA, Shafie M, Shahid S, Sham S, Shamim MA, Shamshirgaran MA, Shastry RP, Sherchan SP, Shiferaw D, Shittu A, Siddig EE, Sinto R, Sood A, Sorensen RJD, Stergachis A, Stoeva TZ, Swain CK, Szarpak L, Tamuzi JL, Temsah MH, Tessema MBT, Thangaraju P, Tran NM, Tran NH, Tumurkhuu M, Ty SS, Udoakang AJ, Ulhaq I, Umar TP, Umer AA, Vahabi SM, Vaithinathan AG, Van den Eynde J, Walson JL, Waqas M, Xing Y, Yadav MK, Yahya G, Yon DK, Zahedi Bialvaei A, Zakham F, Zeleke AM, Zhai C, Zhang Z, Zhang H, Zielińska M, Zheng P, Aravkin AY, Vos T, Hay SI, Mosser JF, Lim SS, Naghavi M, Murray CJL, Kyu HH. Global, regional, and national incidence and mortality burden of non-COVID-19 lower respiratory infections and aetiologies, 1990-2021: a systematic analysis from the Global Burden of Disease Study 2021. THE LANCET. INFECTIOUS DISEASES 2024; 24:974-1002. [PMID: 38636536 PMCID: PMC11339187 DOI: 10.1016/s1473-3099(24)00176-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/16/2023] [Revised: 02/19/2024] [Accepted: 03/07/2024] [Indexed: 04/20/2024]
Abstract
BACKGROUND Lower respiratory infections (LRIs) are a major global contributor to morbidity and mortality. In 2020-21, non-pharmaceutical interventions associated with the COVID-19 pandemic reduced not only the transmission of SARS-CoV-2, but also the transmission of other LRI pathogens. Tracking LRI incidence and mortality, as well as the pathogens responsible, can guide health-system responses and funding priorities to reduce future burden. We present estimates from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 of the burden of non-COVID-19 LRIs and corresponding aetiologies from 1990 to 2021, inclusive of pandemic effects on the incidence and mortality of select respiratory viruses, globally, regionally, and for 204 countries and territories. METHODS We estimated mortality, incidence, and aetiology attribution for LRI, defined by the GBD as pneumonia or bronchiolitis, not inclusive of COVID-19. We analysed 26 259 site-years of mortality data using the Cause of Death Ensemble model to estimate LRI mortality rates. We analysed all available age-specific and sex-specific data sources, including published literature identified by a systematic review, as well as household surveys, hospital admissions, health insurance claims, and LRI mortality estimates, to generate internally consistent estimates of incidence and prevalence using DisMod-MR 2.1. For aetiology estimation, we analysed multiple causes of death, vital registration, hospital discharge, microbial laboratory, and literature data using a network analysis model to produce the proportion of LRI deaths and episodes attributable to the following pathogens: Acinetobacter baumannii, Chlamydia spp, Enterobacter spp, Escherichia coli, fungi, group B streptococcus, Haemophilus influenzae, influenza viruses, Klebsiella pneumoniae, Legionella spp, Mycoplasma spp, polymicrobial infections, Pseudomonas aeruginosa, respiratory syncytial virus (RSV), Staphylococcus aureus, Streptococcus pneumoniae, and other viruses (ie, the aggregate of all viruses studied except influenza and RSV), as well as a residual category of other bacterial pathogens. FINDINGS Globally, in 2021, we estimated 344 million (95% uncertainty interval [UI] 325-364) incident episodes of LRI, or 4350 episodes (4120-4610) per 100 000 population, and 2·18 million deaths (1·98-2·36), or 27·7 deaths (25·1-29·9) per 100 000. 502 000 deaths (406 000-611 000) were in children younger than 5 years, among which 254 000 deaths (197 000-320 000) occurred in countries with a low Socio-demographic Index. Of the 18 modelled pathogen categories in 2021, S pneumoniae was responsible for the highest proportions of LRI episodes and deaths, with an estimated 97·9 million (92·1-104·0) episodes and 505 000 deaths (454 000-555 000) globally. The pathogens responsible for the second and third highest episode counts globally were other viral aetiologies (46·4 million [43·6-49·3] episodes) and Mycoplasma spp (25·3 million [23·5-27·2]), while those responsible for the second and third highest death counts were S aureus (424 000 [380 000-459 000]) and K pneumoniae (176 000 [158 000-194 000]). From 1990 to 2019, the global all-age non-COVID-19 LRI mortality rate declined by 41·7% (35·9-46·9), from 56·5 deaths (51·3-61·9) to 32·9 deaths (29·9-35·4) per 100 000. From 2019 to 2021, during the COVID-19 pandemic and implementation of associated non-pharmaceutical interventions, we estimated a 16·0% (13·1-18·6) decline in the global all-age non-COVID-19 LRI mortality rate, largely accounted for by a 71·8% (63·8-78·9) decline in the number of influenza deaths and a 66·7% (56·6-75·3) decline in the number of RSV deaths. INTERPRETATION Substantial progress has been made in reducing LRI mortality, but the burden remains high, especially in low-income and middle-income countries. During the COVID-19 pandemic, with its associated non-pharmaceutical interventions, global incident LRI cases and mortality attributable to influenza and RSV declined substantially. Expanding access to health-care services and vaccines, including S pneumoniae, H influenzae type B, and novel RSV vaccines, along with new low-cost interventions against S aureus, could mitigate the LRI burden and prevent transmission of LRI-causing pathogens. FUNDING Bill & Melinda Gates Foundation, Wellcome Trust, and Department of Health and Social Care (UK).
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Ohsawa Y, Sun Y, Sekiguchi K, Kondo S, Maekawa T, Takita M, Tanimoto T, Kami M. Risk Index of Regional Infection Expansion of COVID-19: Moving Direction Entropy Study Using Mobility Data and Its Application to Tokyo. JMIR Public Health Surveill 2024; 10:e57742. [PMID: 39037745 PMCID: PMC11375397 DOI: 10.2196/57742] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2024] [Revised: 06/03/2024] [Accepted: 07/21/2024] [Indexed: 07/23/2024] Open
Abstract
BACKGROUND Policies, such as stay home, bubbling, and stay with your community, recommending that individuals reduce contact with diverse communities, including families and schools, have been introduced to mitigate the spread of the COVID-19 pandemic. However, these policies are violated if individuals from various communities gather, which is a latent risk in a real society where people move among various unreported communities. OBJECTIVE We aimed to create a physical index to assess the possibility of contact between individuals from diverse communities, which serves as an indicator of the potential risk of SARS-CoV-2 spread when considered and combined with existing indices. METHODS Moving direction entropy (MDE), which quantifies the diversity of moving directions of individuals in each local region, is proposed as an index to evaluate a region's risk of contact of individuals from diverse communities. MDE was computed for each inland municipality in Tokyo using mobility data collected from smartphones before and during the COVID-19 pandemic. To validate the hypothesis that the impact of intercommunity contact on infection expansion becomes larger for a virus with larger infectivity, we compared the correlations of the expansion of infectious diseases with indices, including MDE and the densities of supermarkets, restaurants, etc. In addition, we analyzed the temporal changes in MDE in municipalities. RESULTS This study had 4 important findings. First, the MDE values for local regions showed significant invariance between different periods according to the Spearman rank correlation coefficient (>0.9). Second, MDE was found to correlate with the rate of infection cases of COVID-19 among local populations in 53 inland regions (average of 0.76 during the period of expansion). The density of restaurants had a similar correlation with COVID-19. The correlation between MDE and the rate of infection was smaller for influenza than for COVID-19, and tended to be even smaller for sexually transmitted diseases (order of infectivity). These findings support the hypothesis. Third, the spread of COVID-19 was accelerated in regions with high-rank MDE values compared to those with high-rank restaurant densities during and after the period of the governmental declaration of emergency (P<.001). Fourth, the MDE values tended to be high and increased during the pandemic period in regions where influx or daytime movement was present. A possible explanation for the third and fourth findings is that policymakers and living people have been overlooking MDE. CONCLUSIONS We recommend monitoring the regional values of MDE to reduce the risk of infection spread. To aid in this monitoring, we present a method to create a heatmap of MDE values, thereby drawing public attention to behaviors that facilitate contact between communities during a highly infectious disease pandemic.
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Affiliation(s)
- Yukio Ohsawa
- School of Engineering, The University of Tokyo, Tokyo, Japan
| | - Yi Sun
- School of Engineering, The University of Tokyo, Tokyo, Japan
| | - Kaira Sekiguchi
- School of Engineering, The University of Tokyo, Tokyo, Japan
| | - Sae Kondo
- School of Engineering, Mie University, Tsu, Japan
- RCAST, The University of Tokyo, Tokyo, Japan
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Chang MC, Wen TH. The Mediating Role of Human Mobility in Temporal-Lagged Relationships Between Risk Perception and COVID-19 Dynamics in Taiwan: Statistical Modeling for Comparing the Pre-Omicron and Omicron Eras. JMIR Public Health Surveill 2024; 10:e55183. [PMID: 39166531 DOI: 10.2196/55183] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2023] [Revised: 06/10/2024] [Accepted: 06/12/2024] [Indexed: 08/23/2024] Open
Abstract
Background The COVID-19 pandemic has profoundly impacted all aspects of human life for over 3 years. Understanding the evolution of public risk perception during these periods is crucial. Few studies explore the mechanisms for reducing disease transmission due to risk perception. Thus, we hypothesize that changes in human mobility play a mediating role between risk perception and the progression of the pandemic. Objective The study aims to explore how various forms of human mobility, including essential, nonessential, and job-related behaviors, mediate the temporal relationships between risk perception and pandemic dynamics. Methods We used distributed-lag linear structural equation models to compare the mediating impact of human mobility across different virus variant periods. These models examined the temporal dynamics and time-lagged effects among risk perception, changes in mobility, and virus transmission in Taiwan, focusing on two distinct periods: (1) April-August 2021 (pre-Omicron era) and (2) February-September 2022 (Omicron era). Results In the pre-Omicron era, our findings showed that an increase in public risk perception correlated with significant reductions in COVID-19 cases across various types of mobility within specific time frames. Specifically, we observed a decrease of 5.59 (95% CI -4.35 to -6.83) COVID-19 cases per million individuals after 7 weeks in nonessential mobility, while essential mobility demonstrated a reduction of 10.73 (95% CI -9.6030 to -11.8615) cases after 8 weeks. Additionally, job-related mobility resulted in a decrease of 3.96 (95% CI -3.5039 to -4.4254) cases after 11 weeks. However, during the Omicron era, these effects notably diminished. A reduction of 0.85 (95% CI -1.0046 to -0.6953) cases through nonessential mobility after 10 weeks and a decrease of 0.69 (95% CI -0.7827 to -0.6054) cases through essential mobility after 12 weeks were observed. Conclusions This study confirms that changes in mobility serve as a mediating factor between heightened risk perception and pandemic mitigation in both pre-Omicron and Omicron periods. This suggests that elevating risk perception is notably effective in impeding virus progression, especially when vaccines are unavailable or their coverage remains limited. Our findings provide significant value for health authorities in devising policies to address the global threats posed by emerging infectious diseases.
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Affiliation(s)
- Min-Chien Chang
- Department of Geography, National Taiwan University, Taipei, Taiwan
| | - Tzai-Hung Wen
- Department of Geography, National Taiwan University, Taipei, Taiwan
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Cori A, Kucharski A. Inference of epidemic dynamics in the COVID-19 era and beyond. Epidemics 2024; 48:100784. [PMID: 39167954 DOI: 10.1016/j.epidem.2024.100784] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2024] [Revised: 06/25/2024] [Accepted: 07/11/2024] [Indexed: 08/23/2024] Open
Abstract
The COVID-19 pandemic demonstrated the key role that epidemiology and modelling play in analysing infectious threats and supporting decision making in real-time. Motivated by the unprecedented volume and breadth of data generated during the pandemic, we review modern opportunities for analysis to address questions that emerge during a major modern epidemic. Following the broad chronology of insights required - from understanding initial dynamics to retrospective evaluation of interventions, we describe the theoretical foundations of each approach and the underlying intuition. Through a series of case studies, we illustrate real life applications, and discuss implications for future work.
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Affiliation(s)
- Anne Cori
- MRC Centre for Global Infectious Disease Analysis, Jameel Institute, School of Public Health, Imperial College London, United Kingdom.
| | - Adam Kucharski
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, United Kingdom.
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Tang C, Todo Y, Kodera S, Sun R, Shimada A, Hirata A. A novel multivariate time series forecasting dendritic neuron model for COVID-19 pandemic transmission tendency. Neural Netw 2024; 179:106527. [PMID: 39029298 DOI: 10.1016/j.neunet.2024.106527] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2022] [Revised: 02/21/2024] [Accepted: 07/07/2024] [Indexed: 07/21/2024]
Abstract
A novel coronavirus discovered in late 2019 (COVID-19) quickly spread into a global epidemic and, thankfully, was brought under control by 2022. Because of the virus's unknown mutations and the vaccine's waning potency, forecasting is still essential for resurgence prevention and medical resource management. Computational efficiency and long-term accuracy are two bottlenecks for national-level forecasting. This study develops a novel multivariate time series forecasting model, the densely connected highly flexible dendritic neuron model (DFDNM) to predict daily and weekly positive COVID-19 cases. DFDNM's high flexibility mechanism improves its capacity to deal with nonlinear challenges. The dense introduction of shortcut connections alleviates the vanishing and exploding gradient problems, encourages feature reuse, and improves feature extraction. To deal with the rapidly growing parameters, an improved variation of the adaptive moment estimation (AdamW) algorithm is employed as the learning algorithm for the DFDNM because of its strong optimization ability. The experimental results and statistical analysis conducted across three Japanese prefectures confirm the efficacy and feasibility of the DFDNM while outperforming various state-of-the-art machine learning models. To the best of our knowledge, the proposed DFDNM is the first to restructure the dendritic neuron model's neural architecture, demonstrating promising use in multivariate time series prediction. Because of its optimal performance, the DFDNM may serve as an important reference for national and regional government decision-makers aiming to optimize pandemic prevention and medical resource management. We also verify that DFDMN is efficiently applicable not only to COVID-19 transmission prediction, but also to more general multivariate prediction tasks. It leads us to believe that it might be applied as a promising prediction model in other fields.
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Affiliation(s)
- Cheng Tang
- Faculty of Information Science and Electrical Engineering, Kyushu University, Fukuoka, 819-0395, Japan; Department of Electrical and Mechanical Engineering, Nagoya Institute of Technology, Nagoya-shi, 466-8555, Japan.
| | - Yuki Todo
- Faculty of Electrical and Computer Engineering, Kanazawa University, Kanazawa-shi, 920-1192, Japan
| | - Sachiko Kodera
- Department of Electrical and Mechanical Engineering, Nagoya Institute of Technology, Nagoya-shi, 466-8555, Japan
| | - Rong Sun
- Faculty of Electrical and Computer Engineering, Kanazawa University, Kanazawa-shi, 920-1192, Japan; Division of Medical Oncology & Respiratory Medicine, Department of Internal Medicine, Faculty of Medicine, Shimane University, Izumo, Japan
| | - Atsushi Shimada
- Faculty of Information Science and Electrical Engineering, Kyushu University, Fukuoka, 819-0395, Japan
| | - Akimasa Hirata
- Department of Electrical and Mechanical Engineering, Nagoya Institute of Technology, Nagoya-shi, 466-8555, Japan.
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Zhang Y, Xiao C, Huang G, Shen M. Impacts of Major Changes in China's COVID-19 Prevention and Control Policies on Emergency Department Visits: A Quasi-Experiment. Risk Manag Healthc Policy 2024; 17:1771-1778. [PMID: 38974390 PMCID: PMC11227878 DOI: 10.2147/rmhp.s463062] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2024] [Accepted: 06/21/2024] [Indexed: 07/09/2024] Open
Abstract
Objective This study aims to evaluate the impact of COVID-19 prevention and control policies on the frequency of emergency department (ED) visits in a large tertiary hospital in central China, from January 2018 to September 2023. Methods We conducted a multi-stage interrupted time series analysis to investigate the impact of various epidemic control policies on weekly ED visits at a tertiary hospital in Hunan Province, China. The study period ranged from January 1, 2018, to September 30, 2023, and was divided into four distinct periods: pre-epidemic, pandemic, normalized control, and end of control. Using a quasi-Poisson regression model, we examined the specific effects of these policies on emergency visits, with a particular focus on stratifying patients based on respiratory versus non-respiratory diseases. Results Compared to the pre-pandemic period, the number of ED visits in a tertiary hospital decreased by 38.5% (95% CI: 25.1% to 49.8%) during the COVID-19 pandemic, of which the number of ED visits for respiratory diseases increased by 79.4% (95% CI: 13.2% to 177.2%) and the number of ED visits for non-respiratory diseases decreased by 45.9% (95% CI: -55.7% to -34.2%). After the end of the epidemic control, the total number of ED visits increased by 31.5% (95% CI: 19.1% to 45.0%), with the number of ED visits for respiratory diseases rising by 379.2% (95% CI: 275.9% to 511.8%), but with no significant change in the number of ED visits for non-respiratory emergencies. Conclusion Control policies were associated with people avoiding emergency care for non-respiratory related reasons during the pandemic, while the end of control policies was associated with a sharp rise in emergency care for respiratory diseases. This study provides a scientific basis for the different changes in ED visits under the implementation of varying epidemic prevention and control policies.
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Affiliation(s)
- Yang Zhang
- Department of Social Medicine and Health Management, Xiangya School of Public Health, Central South University, Changsha City, Hunan Province, People’s Republic of China
| | - Chenggen Xiao
- Department of Emergency, Xiangya Hospital, Central South University, Changsha City, Hunan Province, People’s Republic of China
| | - Guoqing Huang
- Department of Emergency, Xiangya Hospital, Central South University, Changsha City, Hunan Province, People’s Republic of China
| | - Minxue Shen
- Department of Social Medicine and Health Management, Xiangya School of Public Health, Central South University, Changsha City, Hunan Province, People’s Republic of China
- Furong Laboratory, Central South University, Changsha City, Hunan Province, People’s Republic of China
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Xu Y, Dong C, Shao W. Culture and effectiveness of distance restriction policies: evidence from the COVID-19 pandemic. J R Soc Interface 2024; 21:20240159. [PMID: 39081112 PMCID: PMC11289640 DOI: 10.1098/rsif.2024.0159] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2024] [Revised: 05/29/2024] [Accepted: 06/25/2024] [Indexed: 08/02/2024] Open
Abstract
Natural disasters bring indelible negative impacts to human beings, and people usually adopt some post hoc strategies to alleviate such impacts. However, the same strategies may have different effects in different countries (or regions), which is rarely paid attention by the academic community. In the context of COVID-19, we examine the effect of distance restriction policies (DRP) on reducing human mobility and thus inhibiting the spread of the virus. By establishing a multi-period difference-in-differences model to analyse the unique panel dataset constructed by 44 countries, we show that DRP does significantly reduce mobility, but the effectiveness varies from country to country. We built a moderating effect model to explain the differences from the cultural perspective and found that DRP can be more effective in reducing human mobility in countries with a lower indulgence index. The results remain robust when different sensitivity analyses are performed. Our conclusions call for governments to adapt their policies to the impact of disasters rather than copy each other.
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Affiliation(s)
- Yang Xu
- School of Finance, Anhui University of Finance and Economics, Bengbu, People’s Republic of China
| | - Chen Dong
- School of Finance, Anhui University of Finance and Economics, Bengbu, People’s Republic of China
| | - Wenjing Shao
- School of Finance, Anhui University of Finance and Economics, Bengbu, People’s Republic of China
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9
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Paltra S, Bostanci I, Nagel K. The effect of mobility reductions on infection growth is quadratic in many cases. Sci Rep 2024; 14:14475. [PMID: 38914583 PMCID: PMC11196635 DOI: 10.1038/s41598-024-64230-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2024] [Accepted: 06/06/2024] [Indexed: 06/26/2024] Open
Abstract
Stay-at-home orders were introduced in many countries during the COVID-19 pandemic, limiting the time people spent outside their home and the attendance of gatherings. In this study, we argue from a theoretical model that in many cases the effect of such stay-at-home orders on incidence growth should be quadratic, and that this statement should also hold beyond COVID-19. That is, a reduction of the out-of-home duration to, say, 70% of its original value should reduce incidence growth and thus the effective R-value to 70 % · 70 % = 49 % of its original value. We then show that this hypothesis can be substantiated from data acquired during the COVID-19 pandemic by using a multiple regression model to fit a combination of the quadratic out-of-home duration and temperature to the COVID-19 growth multiplier. We finally demonstrate that many other models, when brought to the same scale, give similar reductions of the effective R-value, but that none of these models extend plausibly to an out-of-home duration of zero.
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Affiliation(s)
- Sydney Paltra
- Technische Universität Berlin, FG Verkehrssystemplanung und Verkehrstelematik, 10623, Berlin, Germany.
| | | | - Kai Nagel
- Technische Universität Berlin, FG Verkehrssystemplanung und Verkehrstelematik, 10623, Berlin, Germany
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10
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Lima HS, Tupinambás U, Guimarães FG. Estimating time-varying epidemiological parameters and underreporting of Covid-19 cases in Brazil using a mathematical model with fuzzy transitions between epidemic periods. PLoS One 2024; 19:e0305522. [PMID: 38885221 PMCID: PMC11182538 DOI: 10.1371/journal.pone.0305522] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2023] [Accepted: 06/01/2024] [Indexed: 06/20/2024] Open
Abstract
Our study conducts a comprehensive analysis of the Covid-19 pandemic in Brazil, spanning five waves over three years. We employed a novel Susceptible-Infected-Recovered-Dead-Susceptible (SIRDS) model with a fuzzy transition between epidemic periods to estimate time-varying parameters and evaluate case underreporting. The initial basic reproduction number (R0) is identified at 2.44 (95% Confidence Interval (CI): 2.42-2.46), decreasing to 1.00 (95% CI: 0.99-1.01) during the first wave. The model estimates an underreporting factor of 12.9 (95% CI: 12.5-13.2) more infections than officially reported by Brazilian health authorities, with an increasing factor of 5.8 (95% CI: 5.2-6.4), 12.9 (95% CI: 12.5-13.3), and 16.8 (95% CI: 15.8-17.5) in 2020, 2021, and 2022 respectively. Additionally, the Infection Fatality Rate (IFR) is initially 0.88% (95% CI: 0.81%-0.94%) during the initial phase but consistently reduces across subsequent outbreaks, reaching its lowest value of 0.018% (95% CI: 0.011-0.033) in the last outbreak. Regarding the immunity period, the observed uncertainty and low sensitivity indicate that inferring this parameter is particularly challenging. Brazil successfully reduced R0 during the first wave, coinciding with decreased human mobility. Ineffective public health measures during the second wave resulted in the highest mortality rates within the studied period. We attribute lower mortality rates in 2022 to increased vaccination coverage and the lower lethality of the Omicron variant. We demonstrate the model generalization by its application to other countries. Comparative analyses with serological research further validate the accuracy of the model. In forecasting analysis, our model provides reasonable outbreak predictions. In conclusion, our study provides a nuanced understanding of the Covid-19 pandemic in Brazil, employing a novel epidemiological model. The findings contribute to the broader discourse on pandemic dynamics, underreporting, and the effectiveness of health interventions.
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Affiliation(s)
- Hélder Seixas Lima
- Instituto Federal do Norte de Minas Gerais, Januária, MG, Brazil
- Graduate Program in Electrical Engineering, Universidade Federal de Minas Gerais, Belo Horizonte, MG, Brazil
| | - Unaí Tupinambás
- Department of Medical Clinic, Universidade Federal de Minas Gerais, Belo Horizonte, MG, Brazil
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11
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Morgenstern C, Laydon DJ, Whittaker C, Mishra S, Haw D, Bhatt S, Ferguson NM. The interaction of disease transmission, mortality, and economic output over the first 2 years of the COVID-19 pandemic. PLoS One 2024; 19:e0301785. [PMID: 38870106 PMCID: PMC11175517 DOI: 10.1371/journal.pone.0301785] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2023] [Accepted: 03/21/2024] [Indexed: 06/15/2024] Open
Abstract
BACKGROUND The COVID-19 pandemic has caused over 7.02 million deaths as of January 2024 and profoundly affected most countries' Gross Domestic Product (GDP). Here, we study the interaction of SARS-CoV-2 transmission, mortality, and economic output between January 2020 and December 2022 across 25 European countries. METHODS We use a Bayesian mixed effects model with auto-regressive terms to estimate the temporal relationships between disease transmission, excess deaths, changes in economic output, transit mobility and non-pharmaceutical interventions (NPIs) across countries. RESULTS Disease transmission intensity (logRt) decreases GDP and increases excess deaths, where the latter association is longer-lasting. Changes in GDP as well as prior week transmission intensity are both negatively associated with each other (-0.241, 95% CrI: -0.295 - -0.189). We find evidence of risk-averse behaviour, as changes in transit and prior week transmission intensity are negatively associated (-0.055, 95% CrI: -0.074 to -0.036). Our results highlight a complex cost-benefit trade-off from individual NPIs. For example, banning international travel is associated with both increases in GDP (0.014, 0.002-0.025) and decreases in excess deaths (-0.014, 95% CrI: -0.028 - -0.001). Country-specific random effects, such as the poverty rate, are positively associated with excess deaths while the UN government effectiveness index is negatively associated with excess deaths. INTERPRETATION The interplay between transmission intensity, excess deaths, population mobility and economic output is highly complex, and none of these factors can be considered in isolation. Our results reinforce the intuitive idea that significant economic activity arises from diverse person-to-person interactions. Our analysis quantifies and highlights that the impact of disease on a given country is complex and multifaceted. Long-term economic impairments are not fully captured by our model, as well as long-term disease effects (Long COVID).
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Affiliation(s)
- Christian Morgenstern
- MRC Centre for Global Infectious Disease Analysis & WHO Collaborating Centre for Infectious Disease Modelling, Jameel Institute, School of Public Health, Imperial College London, London, United Kingdom
| | - Daniel J. Laydon
- MRC Centre for Global Infectious Disease Analysis & WHO Collaborating Centre for Infectious Disease Modelling, Jameel Institute, School of Public Health, Imperial College London, London, United Kingdom
| | - Charles Whittaker
- MRC Centre for Global Infectious Disease Analysis & WHO Collaborating Centre for Infectious Disease Modelling, Jameel Institute, School of Public Health, Imperial College London, London, United Kingdom
| | - Swapnil Mishra
- MRC Centre for Global Infectious Disease Analysis & WHO Collaborating Centre for Infectious Disease Modelling, Jameel Institute, School of Public Health, Imperial College London, London, United Kingdom
- University of Copenhagen, Copenhagen, Denmark
| | - David Haw
- MRC Centre for Global Infectious Disease Analysis & WHO Collaborating Centre for Infectious Disease Modelling, Jameel Institute, School of Public Health, Imperial College London, London, United Kingdom
| | - Samir Bhatt
- MRC Centre for Global Infectious Disease Analysis & WHO Collaborating Centre for Infectious Disease Modelling, Jameel Institute, School of Public Health, Imperial College London, London, United Kingdom
- University of Copenhagen, Copenhagen, Denmark
| | - Neil M. Ferguson
- MRC Centre for Global Infectious Disease Analysis & WHO Collaborating Centre for Infectious Disease Modelling, Jameel Institute, School of Public Health, Imperial College London, London, United Kingdom
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12
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Alrawas L, Tridane A, Benrhmach G. A novel approach to model the role of mobility suppression and vaccinations in containing epidemics in a network of cities. Infect Dis Model 2024; 9:397-410. [PMID: 38385016 PMCID: PMC10879667 DOI: 10.1016/j.idm.2024.01.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2023] [Revised: 12/12/2023] [Accepted: 01/14/2024] [Indexed: 02/23/2024] Open
Abstract
This paper presents a comprehensive agent-based model for the spread of an infection in a network of cities. Directional mobility is defined between each two cities and can take different values. The work examines the role that such mobility levels play in containing the infection with various vaccination coverage and age distributions. The results indicate that mobility reduction is sufficient to control the disease under all circumstances and full lockdowns are not a necessity. It has to be reduced to different ratios depending on the vaccination level and age distribution. A key finding is that increasing vaccination coverage above a certain level does not affect the mobility suppression level required to control the infection anymore for the cases of young population and heterogeneous age distributions. By investigating several migration and commuting patterns, it is found that shutting mobility in a few local places is favored against reducing mobility over the entire country network. In addition, commuting -and not migration-influences the spread level of the infection. The work offers an exclusive combined network-based and agent-based model that makes use of randomly generated mobility matrices.
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Affiliation(s)
- Leen Alrawas
- Department of Physics, New York University Abu Dhabi, Abu Dhabi, Abu Dhabi, United Arab Emirates
| | - Abdessamad Tridane
- Department of Mathematical Sciences, United Arab Emirates University, Al Ain, Abu Dhabi, United Arab Emirates
- Emirates Center for Mobility Research, United Arab Emirates University, Al Ain, Abu Dhabi, United Arab Emirates
| | - Ghassane Benrhmach
- Department of Statistics and Business Analytics, United Arab Emirates University, Al Ain, Abu Dhabi, United Arab Emirates
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13
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Soucy JPR, Sturrock SL, Berry I, Westwood DJ, Daneman N, Fisman D, MacFadden DR, Brown KA. Public transit mobility as a leading indicator of COVID-19 transmission in 40 cities during the first wave of the pandemic. PeerJ 2024; 12:e17455. [PMID: 38832041 PMCID: PMC11146320 DOI: 10.7717/peerj.17455] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2024] [Accepted: 05/03/2024] [Indexed: 06/05/2024] Open
Abstract
Background The rapid global emergence of the COVID-19 pandemic in early 2020 created urgent demand for leading indicators to track the spread of the virus and assess the consequences of public health measures designed to limit transmission. Public transit mobility, which has been shown to be responsive to previous societal disruptions such as disease outbreaks and terrorist attacks, emerged as an early candidate. Methods We conducted a longitudinal ecological study of the association between public transit mobility reductions and COVID-19 transmission using publicly available data from a public transit app in 40 global cities from March 16 to April 12, 2020. Multilevel linear regression models were used to estimate the association between COVID-19 transmission and the value of the mobility index 2 weeks prior using two different outcome measures: weekly case ratio and effective reproduction number. Results Over the course of March 2020, median public transit mobility, measured by the volume of trips planned in the app, dropped from 100% (first quartile (Q1)-third quartile (Q3) = 94-108%) of typical usage to 10% (Q1-Q3 = 6-15%). Mobility was strongly associated with COVID-19 transmission 2 weeks later: a 10% decline in mobility was associated with a 12.3% decrease in the weekly case ratio (exp(β) = 0.877; 95% confidence interval (CI): [0.859-0.896]) and a decrease in the effective reproduction number (β = -0.058; 95% CI: [-0.068 to -0.048]). The mobility-only models explained nearly 60% of variance in the data for both outcomes. The adjustment for epidemic timing attenuated the associations between mobility and subsequent COVID-19 transmission but only slightly increased the variance explained by the models. Discussion Our analysis demonstrated the value of public transit mobility as a leading indicator of COVID-19 transmission during the first wave of the pandemic in 40 global cities, at a time when few such indicators were available. Factors such as persistently depressed demand for public transit since the onset of the pandemic limit the ongoing utility of a mobility index based on public transit usage. This study illustrates an innovative use of "big data" from industry to inform the response to a global pandemic, providing support for future collaborations aimed at important public health challenges.
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Affiliation(s)
- Jean-Paul R. Soucy
- Division of Epidemiology, Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
| | - Shelby L. Sturrock
- Division of Epidemiology, Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
| | - Isha Berry
- Division of Epidemiology, Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
| | | | - Nick Daneman
- Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada
- Department of Medicine, University of Toronto, Toronto, Ontario, Canada
- ICES, Toronto, Ontario, Canada
| | - David Fisman
- Division of Epidemiology, Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
| | | | - Kevin A. Brown
- Division of Epidemiology, Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
- ICES, Toronto, Ontario, Canada
- Public Health Ontario, Toronto, Ontario, Canada
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14
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Omori R, Ito K, Kanemitsu S, Kimura R, Iwasa Y. Human movement avoidance decisions during Coronavirus disease 2019 in Japan. J Theor Biol 2024; 585:111795. [PMID: 38493888 DOI: 10.1016/j.jtbi.2024.111795] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2023] [Revised: 03/12/2024] [Accepted: 03/14/2024] [Indexed: 03/19/2024]
Abstract
Understanding host behavioral change in response to epidemics is important to forecast the disease dynamics. To predict the behavioral change relevant to the epidemic situation (e.g., the number of reported cases), we need to know the epidemic situation at the moment of decision, which is difficult to identify from the records of actually performed human mobility. In this study, the largest travel accommodation reservation data covering half of the existed accommodations in Japan was analyzed to observe decision-making timings and how it responded to the changing epidemic situation during Japan's Coronavirus Disease 2019 until February 2023. To this end, we measured mobility avoidance index proposed in Ito et al., 2022 to indicate people's decision of mobility avoidance and quantified it using the time-series of the accommodation booking/cancellation data. We observed matches of the peak dates of the mobility avoidance and the number of reported cases, and mobility avoidance changed proportional to the logarithmic number of reported cases. We also found that the slope of mobility avoidance against the change of the logarithmic number of reported cases were similar among the epidemic waves, while the intercept of that was much reduced as the first epidemic wave passed by. People measure the intensity of epidemic by logarithm of the number of reported cases. The sensitivity of their response is established during the first wave and the people's response became weakened after the first experience, as if the number of reported cases were multiplied by a constant small factor.
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Affiliation(s)
- Ryosuke Omori
- Division of Bioinformatics, International Institute for Zoonosis Control, Hokkaido University, Sapporo, Hokkaido 001-0020, Japan.
| | - Koichi Ito
- Division of Bioinformatics, International Institute for Zoonosis Control, Hokkaido University, Sapporo, Hokkaido 001-0020, Japan; Faculty of Environmental Earth Science, Hokkaido University, Sapporo, Hokkaido 060-0810, Japan
| | - Shunsuke Kanemitsu
- Data Solution Unit 2(Marriage & Family/Automobile Business/Travel), Data Management & Planning Office, Product Development Management Office, Recruit Co., Ltd, Chiyoda-ku, Tokyo 100-6640, Japan
| | - Ryusuke Kimura
- SaaS Data Solution Unit, Data Management & Planning Office, Product Development Management Office, Recruit Co., Ltd, Chiyoda-ku, Tokyo 100-6640, Japan
| | - Yoh Iwasa
- Department of Biology, Faculty of Science, Kyushu University, 744 Motooka, Nishi-ku, Fukuoka 819-0395, Japan
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15
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Chong KC, Zhao S, Hung CT, Jia KM, Ho JYE, Lam HCY, Jiang X, Li C, Lin G, Yam CHK, Chow TY, Wang Y, Li K, Wang H, Wei Y, Guo Z, Yeoh EK. Association between meteorological variations and the superspreading potential of SARS-CoV-2 infections. ENVIRONMENT INTERNATIONAL 2024; 188:108762. [PMID: 38776652 DOI: 10.1016/j.envint.2024.108762] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/17/2024] [Revised: 04/25/2024] [Accepted: 05/18/2024] [Indexed: 05/25/2024]
Abstract
BACKGROUND While many investigations examined the association between environmental covariates and COVID-19 incidence, none have examined their relationship with superspreading, a characteristic describing very few individuals disproportionally infecting a large number of people. METHODS Contact tracing data of all the laboratory-confirmed COVID-19 cases in Hong Kong from February 16, 2020 to April 30, 2021 were used to form the infection clusters for estimating the time-varying dispersion parameter (kt), a measure of superspreading potential. Generalized additive models with identity link function were used to examine the association between negative-log kt (larger means higher superspreading potential) and the environmental covariates, adjusted with mobility metrics that account for the effect of social distancing measures. RESULTS A total of 6,645 clusters covering 11,717 cases were reported over the study period. After centering at the median temperature, a lower ambient temperature at 10th percentile (18.2 °C) was significantly associated with a lower estimate of negative-log kt (adjusted expected change: -0.239 [95 % CI: -0.431 to -0.048]). While a U-shaped relationship between relative humidity and negative-log kt was observed, an inverted U-shaped relationship with actual vapour pressure was found. A higher total rainfall was significantly associated with lower estimates of negative-log kt. CONCLUSIONS This study demonstrated a link between meteorological factors and the superspreading potential of COVID-19. We speculated that cold weather and rainy days reduced the social activities of individuals minimizing the interaction with others and the risk of spreading the diseases in high-risk facilities or large clusters, while the extremities of relative humidity may favor the stability and survival of the SARS-CoV-2 virus.
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Affiliation(s)
- Ka Chun Chong
- Centre for Health Systems and Policy Research, The Chinese University of Hong Kong, Hong Kong Special Administrative Region of China; The School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong Special Administrative Region of China
| | - Shi Zhao
- Centre for Health Systems and Policy Research, The Chinese University of Hong Kong, Hong Kong Special Administrative Region of China; School of Public Health, Tianjin Medical University, Tianjin, China
| | - Chi Tim Hung
- Centre for Health Systems and Policy Research, The Chinese University of Hong Kong, Hong Kong Special Administrative Region of China; The School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong Special Administrative Region of China
| | - Katherine Min Jia
- Center for Communicable Disease Dynamics, Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, United States
| | - Janice Ying-En Ho
- Division of Landscape Architecture, Department of Architecture, Faculty of Architecture, The University of Hong Kong, Hong Kong Special Administrative Region of China
| | - Holly Ching Yu Lam
- Collaborating Centre for Oxford University and CUHK for Disaster and Medical Humanitarian Response, The Chinese University of Hong Kong, Hong Kong Special Administrative Region of China
| | - Xiaoting Jiang
- The School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong Special Administrative Region of China
| | - Conglu Li
- The School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong Special Administrative Region of China
| | - Guozhang Lin
- The School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong Special Administrative Region of China
| | - Carrie Ho Kwan Yam
- Centre for Health Systems and Policy Research, The Chinese University of Hong Kong, Hong Kong Special Administrative Region of China; The School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong Special Administrative Region of China
| | - Tsz Yu Chow
- Centre for Health Systems and Policy Research, The Chinese University of Hong Kong, Hong Kong Special Administrative Region of China; The School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong Special Administrative Region of China
| | - Yawen Wang
- Division of Landscape Architecture, Department of Architecture, Faculty of Architecture, The University of Hong Kong, Hong Kong Special Administrative Region of China
| | - Kehang Li
- The School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong Special Administrative Region of China
| | - Huwen Wang
- The School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong Special Administrative Region of China
| | - Yuchen Wei
- Centre for Health Systems and Policy Research, The Chinese University of Hong Kong, Hong Kong Special Administrative Region of China; The School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong Special Administrative Region of China
| | - Zihao Guo
- The School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong Special Administrative Region of China.
| | - Eng Kiong Yeoh
- Centre for Health Systems and Policy Research, The Chinese University of Hong Kong, Hong Kong Special Administrative Region of China; The School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong Special Administrative Region of China
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16
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Cortes-Ramirez J, Wilches-Vega J, Caicedo-Velasquez B, Paris-Pineda O, Sly P. Spatiotemporal hierarchical Bayesian analysis to identify factors associated with COVID-19 in suburban areas in Colombia. Heliyon 2024; 10:e30182. [PMID: 38707376 PMCID: PMC11068642 DOI: 10.1016/j.heliyon.2024.e30182] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2024] [Revised: 04/21/2024] [Accepted: 04/22/2024] [Indexed: 05/07/2024] Open
Abstract
Introduction The pandemic had a profound impact on the provision of health services in Cúcuta, Colombia where the neighbourhood-level risk of Covid-19 has not been investigated. Identifying the sociodemographic and environmental risk factors of Covid-19 in large cities is key to better estimate its morbidity risk and support health strategies targeting specific suburban areas. This study aims to identify the risk factors associated with the risk of Covid-19 in Cúcuta considering inter -spatial and temporal variations of the disease in the city's neighbourhoods between 2020 and 2022. Methods Age-adjusted rate of Covid-19 were calculated in each Cúcuta neighbourhood and each quarter between 2020 and 2022. A hierarchical spatial Bayesian model was used to estimate the risk of Covid-19 adjusting for socioenvironmental factors per neighbourhood across the study period. Two spatiotemporal specifications were compared (a nonparametric temporal trend; with and without space-time interaction). The posterior mean of the spatial and spatiotemporal effects was used to map the Covid-19 risk. Results There were 65,949 Covid-19 cases in the study period with a varying standardized Covid-19 rate that peaked in October-December 2020 and April-June 2021. Both models identified an association of the poverty and stringency indexes, education level and PM10 with Covid-19 although the best fit model with a space-time interaction estimated a strong association with the number of high-traffic roads only. The highest risk of Covid-19 was found in neighbourhoods in west, central, and east Cúcuta. Conclusions The number of high-traffic roads is the most important risk factor of Covid-19 infection in Cucuta. This indicator of mobility and connectivity overrules other socioenvironmental factors when Bayesian models include a space-time interaction. Bayesian spatial models are important tools to identify significant determinants of Covid-19 and identifying at-risk neighbourhoods in large cities. Further research is needed to establish causal links between these factors and Covid-19.
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Affiliation(s)
- J. Cortes-Ramirez
- Centre for Data Science. Queensland University of Technology, Australia
- Faculty of Medical and Health Sciences, University of Santander, Colombia
- Children's Health and Environment Program, Child Health Research Centre, The University of Queensland, Australia
| | - J.D. Wilches-Vega
- Faculty of Medical and Health Sciences, University of Santander, Colombia
| | - B. Caicedo-Velasquez
- Epidemiology Research Group, Faculty of Public Health, University of Antioquia, Colombia
| | - O.M. Paris-Pineda
- Faculty of Medical and Health Sciences, University of Santander, Colombia
| | - P.D. Sly
- Children's Health and Environment Program, Child Health Research Centre, The University of Queensland, Australia
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17
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Sahu KS, Dubin JA, Majowicz SE, Liu S, Morita PP. Revealing the Mysteries of Population Mobility Amid the COVID-19 Pandemic in Canada: Comparative Analysis With Internet of Things-Based Thermostat Data and Google Mobility Insights. JMIR Public Health Surveill 2024; 10:e46903. [PMID: 38506901 PMCID: PMC10993118 DOI: 10.2196/46903] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Revised: 09/27/2023] [Accepted: 01/03/2024] [Indexed: 03/21/2024] Open
Abstract
BACKGROUND The COVID-19 pandemic necessitated public health policies to limit human mobility and curb infection spread. Human mobility, which is often underestimated, plays a pivotal role in health outcomes, impacting both infectious and chronic diseases. Collecting precise mobility data is vital for understanding human behavior and informing public health strategies. Google's GPS-based location tracking, which is compiled in Google Mobility Reports, became the gold standard for monitoring outdoor mobility during the pandemic. However, indoor mobility remains underexplored. OBJECTIVE This study investigates in-home mobility data from ecobee's smart thermostats in Canada (February 2020 to February 2021) and compares it directly with Google's residential mobility data. By assessing the suitability of smart thermostat data, we aim to shed light on indoor mobility patterns, contributing valuable insights to public health research and strategies. METHODS Motion sensor data were acquired from the ecobee "Donate Your Data" initiative via Google's BigQuery cloud platform. Concurrently, residential mobility data were sourced from the Google Mobility Report. This study centered on 4 Canadian provinces-Ontario, Quebec, Alberta, and British Columbia-during the period from February 15, 2020, to February 14, 2021. Data processing, analysis, and visualization were conducted on the Microsoft Azure platform using Python (Python Software Foundation) and R programming languages (R Foundation for Statistical Computing). Our investigation involved assessing changes in mobility relative to the baseline in both data sets, with the strength of this relationship assessed using Pearson and Spearman correlation coefficients. We scrutinized daily, weekly, and monthly variations in mobility patterns across the data sets and performed anomaly detection for further insights. RESULTS The results revealed noteworthy week-to-week and month-to-month shifts in population mobility within the chosen provinces, aligning with pandemic-driven policy adjustments. Notably, the ecobee data exhibited a robust correlation with Google's data set. Examination of Google's daily patterns detected more pronounced mobility fluctuations during weekdays, a trend not mirrored in the ecobee data. Anomaly detection successfully identified substantial mobility deviations coinciding with policy modifications and cultural events. CONCLUSIONS This study's findings illustrate the substantial influence of the Canadian stay-at-home and work-from-home policies on population mobility. This impact was discernible through both Google's out-of-house residential mobility data and ecobee's in-house smart thermostat data. As such, we deduce that smart thermostats represent a valid tool for facilitating intelligent monitoring of population mobility in response to policy-driven shifts.
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Affiliation(s)
- Kirti Sundar Sahu
- School of Public Health Sciences, University of Waterloo, Waterloo, ON, Canada
| | - Joel A Dubin
- Department of Statistics and Actuarial Science, University of Waterloo, Waterloo, ON, Canada
| | - Shannon E Majowicz
- School of Public Health Sciences, University of Waterloo, Waterloo, ON, Canada
| | - Sam Liu
- School of Exercise Science, Physical and Health Education, University of Victoria, Victoria, BC, Canada
| | - Plinio P Morita
- School of Public Health Sciences, University of Waterloo, Waterloo, ON, Canada
- Institute of Health Policy, Management, and Evaluation, University of Toronto, Toronto, ON, Canada
- Research Institute of Aging, University of Waterloo, Waterloo, ON, Canada
- Department of Systems Design Engineering, University of Waterloo, Waterloo, ON, Canada
- eHealth Innovation, University Health Network, Toronto, ON, Canada
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18
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Anupong S, Chadsuthi S, Hongsing P, Hurst C, Phattharapornjaroen P, Rad S.M. AH, Fernandez S, Huang AT, Vatanaprasan P, Saethang T, Luk-in S, Storer RJ, Ounjai P, Devanga Ragupathi NK, Kanthawee P, Ngamwongsatit N, Badavath VN, Thuptimdang W, Leelahavanichkul A, Kanjanabuch T, Miyanaga K, Cui L, Nanbo A, Shibuya K, Kupwiwat R, Sano D, Furukawa T, Sei K, Higgins PG, Kicic A, Singer AC, Chatsuwan T, Trowsdale S, Abe S, Ishikawa H, Amarasiri M, Modchang C, Wannigama DL. Exploring indoor and outdoor dust as a potential tool for detection and monitoring of COVID-19 transmission. iScience 2024; 27:109043. [PMID: 38375225 PMCID: PMC10875567 DOI: 10.1016/j.isci.2024.109043] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2023] [Revised: 11/09/2023] [Accepted: 01/23/2024] [Indexed: 02/21/2024] Open
Abstract
This study investigated the potential of using SARS-CoV-2 viral concentrations in dust as an additional surveillance tool for early detection and monitoring of COVID-19 transmission. Dust samples were collected from 8 public locations in 16 districts of Bangkok, Thailand, from June to August 2021. SARS-CoV-2 RNA concentrations in dust were quantified, and their correlation with community case incidence was assessed. Our findings revealed a positive correlation between viral concentrations detected in dust and the relative risk of COVID-19. The highest risk was observed with no delay (0-day lag), and this risk gradually decreased as the lag time increased. We observed an overall decline in viral concentrations in public places during lockdown, closely associated with reduced human mobility. The effective reproduction number for COVID-19 transmission remained above one throughout the study period, suggesting that transmission may persist in locations beyond public areas even after the lockdown measures were in place.
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Affiliation(s)
- Suparinthon Anupong
- Biophysics Group, Department of Physics, Faculty of Science, Mahidol University, Bangkok 10400, Thailand
| | - Sudarat Chadsuthi
- Department of Physics, Faculty of Science, Naresuan University, Phitsanulok 65000, Thailand
| | - Parichart Hongsing
- Mae Fah Luang University Hospital, Chiang Rai, Thailand
- School of Integrative Medicine, Mae Fah Luang University, Chiang Rai, Thailand
| | - Cameron Hurst
- Molly Wardaguga Research Centre, Charles Darwin University, Brisbane, QLD, Australia
- Statistics, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
| | - Phatthranit Phattharapornjaroen
- Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok, Thailand
- Institute of Clinical Sciences, Department of Surgery, Sahlgrenska Academy, Gothenburg University, 40530 Gothenburg, Sweden
| | - Ali Hosseini Rad S.M.
- Department of Microbiology and Immunology, University of Otago, Dunedin, Otago 9010, New Zealand
- Center of Excellence in Immunology and Immune-Mediated Diseases, Chulalongkorn University, Bangkok 10330, Thailand
| | - Stefan Fernandez
- Department of Virology, U.S. Army Medical Directorate, Armed Forces Research Institute of Medical Sciences, Bangkok, Thailand
| | - Angkana T. Huang
- Department of Virology, U.S. Army Medical Directorate, Armed Forces Research Institute of Medical Sciences, Bangkok, Thailand
- Department of Genetics, University of Cambridge, Cambridge, UK
| | | | - Thammakorn Saethang
- Department of Computer Science, Faculty of Science, Kasetsart University, Bangkok, Thailand
| | - Sirirat Luk-in
- Department of Clinical Microbiology and Applied Technology, Faculty of Medical Technology, Mahidol University, Bangkok, Thailand
| | - Robin James Storer
- Office of Research Affairs, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
| | - Puey Ounjai
- Department of Biology, Faculty of Science, Mahidol University, Bangkok, Thailand
| | - Naveen Kumar Devanga Ragupathi
- Department of Chemical and Biological Engineering, The University of Sheffield, Sheffield, UK
- Biofilms and Antimicrobial Resistance Consortium of ODA Receiving Countries, The University of Sheffield, Sheffield, UK
- Division of Microbial Interactions, Department of Research and Development, Bioberrys Healthcare and Research Centre, Vellore 632009, India
| | - Phitsanuruk Kanthawee
- Public Health Major, School of Health Science, Mae Fah Luang University, Chiang Rai 57100, Thailand
| | - Natharin Ngamwongsatit
- Department of Clinical Sciences and Public Health, Faculty of Veterinary Science, Mahidol University, Nakhon Pathom, Thailand
| | - Vishnu Nayak Badavath
- School of Pharmacy & Technology Management, SVKM’s Narsee Monjee Institute of Management Studies (NMIMS), Hyderabad 509301, India
| | - Wanwara Thuptimdang
- Institute of Biomedical Engineering, Department of Biomedical Sciences and Biomedical Engineering, Faculty of Medicine, Prince of Songkla University, Hat Yai, Songkhla, Thailand
| | - Asada Leelahavanichkul
- Department of Microbiology, Faculty of Medicine, Chulalongkorn University, King Chulalongkorn Memorial Hospital, Thai Red Cross Society, Bangkok, Thailand
- Translational Research in Inflammation and Immunology Research Unit (TRIRU), Department of Microbiology, Chulalongkorn University, Bangkok, Thailand
| | - Talerngsak Kanjanabuch
- Division of Nephrology, Department of Medicine, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
- Center of Excellence in Kidney Metabolic Disorders, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
- Dialysis Policy and Practice Program (DiP3), School of Global Health, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
- Peritoneal Dialysis Excellence Center, King Chulalongkorn Memorial Hospital, Bangkok, Thailand
| | - Kazuhiko Miyanaga
- Division of Bacteriology, School of Medicine, Jichi Medical University, Tochigi, Japan
| | - Longzhu Cui
- Division of Bacteriology, School of Medicine, Jichi Medical University, Tochigi, Japan
| | - Asuka Nanbo
- The National Research Center for the Control and Prevention of Infectious Diseases, Nagasaki University, Nagasaki, Japan
| | - Kenji Shibuya
- Tokyo Foundation for Policy Research, Minato-ku, Tokyo, Japan
| | - Rosalyn Kupwiwat
- Department of Dermatology. Faculty of Medicine Siriraj Hospital. Mahidol University, Bangkok, Thailand
| | - Daisuke Sano
- Department of Frontier Sciences for Advanced Environment, Graduate School of Environmental Studies, Tohoku University, Sendai, Miyagi, Japan
- Department of Civil and Environmental Engineering, Graduate School of Engineering, Tohoku University, Sendai, Miyagi, Japan
| | - Takashi Furukawa
- Laboratory of Environmental Hygiene, Department of Health Science, School of Allied Health Sciences, Graduate School of Medical Sciences, Kitasato University, Minato City, Tokyo 108-8641, Japan
| | - Kazunari Sei
- Laboratory of Environmental Hygiene, Department of Health Science, School of Allied Health Sciences, Graduate School of Medical Sciences, Kitasato University, Minato City, Tokyo 108-8641, Japan
| | - Paul G. Higgins
- Institute for Medical Microbiology, Immunology and Hygiene, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
- German Centre for Infection Research, Partner Site Bonn-Cologne, Cologne, Germany
| | - Anthony Kicic
- Wal-Yan Respiratory Research Centre, Telethon Kids Institute, University of Western Australia, Nedlands WA 6009, Australia
- Centre for Cell Therapy and Regenerative Medicine, Medical School, The University of Western Australia, Nedlands, WA 6009, Australia
- Department of Respiratory and Sleep Medicine, Perth Children’s Hospital, Nedlands WA 6009, Australia
- School of Population Health, Curtin University, Bentley WA 6102, Australia
| | | | - Tanittha Chatsuwan
- Department of Microbiology, Faculty of Medicine, Chulalongkorn University, King Chulalongkorn Memorial Hospital, Thai Red Cross Society, Bangkok, Thailand
- Center of Excellence in Antimicrobial Resistance and Stewardship, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
| | - Sam Trowsdale
- Department of Environmental Science, University of Auckland, Auckland 1010, New Zealand
| | - Shuichi Abe
- Department of Infectious Diseases and Infection Control, Yamagata Prefectural Central Hospital, Yamagata, Japan
| | - Hitoshi Ishikawa
- Yamagata Prefectural University of Health Sciences, Kamiyanagi, Yamagata 990-2212, Japan
| | - Mohan Amarasiri
- Laboratory of Environmental Hygiene, Department of Health Science, School of Allied Health Sciences, Graduate School of Medical Sciences, Kitasato University, Minato City, Tokyo 108-8641, Japan
| | - Charin Modchang
- Biophysics Group, Department of Physics, Faculty of Science, Mahidol University, Bangkok 10400, Thailand
- Centre of Excellence in Mathematics, MHESI, Bangkok 10400, Thailand
- Thailand Center of Excellence in Physics, Ministry of Higher Education, Science, Research and Innovation, 328 Si Ayutthaya Road, Bangkok 10400, Thailand
| | - Dhammika Leshan Wannigama
- Biofilms and Antimicrobial Resistance Consortium of ODA Receiving Countries, The University of Sheffield, Sheffield, UK
- Department of Microbiology, Faculty of Medicine, Chulalongkorn University, King Chulalongkorn Memorial Hospital, Thai Red Cross Society, Bangkok, Thailand
- Center of Excellence in Antimicrobial Resistance and Stewardship, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
- Department of Infectious Diseases and Infection Control, Yamagata Prefectural Central Hospital, Yamagata, Japan
- Yamagata Prefectural University of Health Sciences, Kamiyanagi, Yamagata 990-2212, Japan
- School of Medicine, Faculty of Health and Medical Sciences, The University of Western Australia, Nedlands, WA, Australia
- Pathogen Hunter’s Research Collaborative Team, Department of Infectious Diseases and Infection Control, Yamagata Prefectural Central Hospital, Yamagata, Japan
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19
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Lee H, Lee G, Kim T, Kim S, Kim H, Lee S. Variability in the serial interval of COVID-19 in South Korea: a comprehensive analysis of age and regional influences. Front Public Health 2024; 12:1362909. [PMID: 38515590 PMCID: PMC10955094 DOI: 10.3389/fpubh.2024.1362909] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2023] [Accepted: 02/23/2024] [Indexed: 03/23/2024] Open
Abstract
Introduction Quantifying the transmissibility over time, particularly by region and age, using parameters such as serial interval and time-varying reproduction number, helps in formulating targeted interventions. Moreover, considering the impact of geographical factors on transmission provides valuable insights into the effectiveness of control measures. Methods Drawing on a comprehensive dataset of COVID-19 cases in South Korea, we analyzed transmission dynamics with a focus on age and regional variations. The dataset, compiled through the efforts of dedicated epidemiologists, includes information on symptom onset dates, enabling detailed investigations. The pandemic was divided into distinct phases, aligning with changes in policies, emergence of variants, and vaccination efforts. We analyzed various interventions such as social distancing, vaccination rates, school closures, and population density. Key parameters like serial interval, heatmaps, and time-varying reproduction numbers were used to quantify age and region-specific transmission trends. Results Analysis of transmission pairs within age groups highlighted the significant impact of school closure policies on the spread among individuals aged 0-19. This analysis also shed light on transmission dynamics within familial and educational settings. Changes in confirmed cases over time revealed a decrease in spread among individuals aged 65 and older, attributed to higher vaccination rates. Conversely, densely populated metropolitan areas experienced an increase in confirmed cases. Examination of time-varying reproduction numbers by region uncovered heterogeneity in transmission patterns, with regions implementing strict social distancing measures showing both increased confirmed cases and delayed spread, indicating the effectiveness of these policies. Discussion Our findings underscore the importance of evaluating and tailoring epidemic control policies based on key COVID-19 parameters. The analysis of social distancing measures, school closures, and vaccine impact provides valuable insights into controlling transmission. By quantifying the impact of these interventions on different age groups and regions, we contribute to the ongoing efforts to combat the COVID-19 pandemic effectively.
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Affiliation(s)
- Hyosun Lee
- Department of Applied Mathematics, Kyung Hee University, Yongin, Republic of Korea
| | - Gira Lee
- Humanitas College, Kyung Hee University, Seoul, Republic of Korea
| | - Tobhin Kim
- Department of Applied Mathematics, Kyung Hee University, Yongin, Republic of Korea
| | - Suhyeon Kim
- Department of Applied Mathematics, Kyung Hee University, Yongin, Republic of Korea
| | - Hyoeun Kim
- Department of Applied Mathematics, Kyung Hee University, Yongin, Republic of Korea
| | - Sunmi Lee
- Department of Applied Mathematics, Kyung Hee University, Yongin, Republic of Korea
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20
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Park SW, Messacar K, Douek DC, Spaulding AB, Metcalf CJE, Grenfell BT. Predicting the impact of COVID-19 non-pharmaceutical intervention on short- and medium-term dynamics of enterovirus D68 in the US. Epidemics 2024; 46:100736. [PMID: 38118274 DOI: 10.1016/j.epidem.2023.100736] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2023] [Revised: 12/02/2023] [Accepted: 12/10/2023] [Indexed: 12/22/2023] Open
Abstract
Recent outbreaks of enterovirus D68 (EV-D68) infections, and their causal linkage with acute flaccid myelitis (AFM), continue to pose a serious public health concern. During 2020 and 2021, the dynamics of EV-D68 and other pathogens have been significantly perturbed by non-pharmaceutical interventions against COVID-19; this perturbation presents a powerful natural experiment for exploring the dynamics of these endemic infections. In this study, we analyzed publicly available data on EV-D68 infections, originally collected through the New Vaccine Surveillance Network, to predict their short- and long-term dynamics following the COVID-19 interventions. Although long-term predictions are sensitive to our assumptions about underlying dynamics and changes in contact rates during the NPI periods, the likelihood of a large outbreak in 2023 appears to be low. Comprehensive surveillance data are needed to accurately characterize future dynamics of EV-D68. The limited incidence of AFM cases in 2022, despite large EV-D68 outbreaks, poses further questions for the timing of the next AFM outbreaks.
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Affiliation(s)
- Sang Woo Park
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA.
| | - Kevin Messacar
- Department of Pediatrics, Section of Infectious Diseases, University of Colorado School of Medicine and Children's Hospital Colorado, Aurora, CO, USA
| | - Daniel C Douek
- Vaccine Research Center, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD 20892, USA
| | - Alicen B Spaulding
- Vaccine Research Center, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD 20892, USA
| | - C Jessica E Metcalf
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA; Princeton School of Public and International Affairs, Princeton University, Princeton, NJ, USA
| | - Bryan T Grenfell
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA; Princeton School of Public and International Affairs, Princeton University, Princeton, NJ, USA
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21
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Scroggins S, Little G, Okala O, Ellis M, Shacham E. The Relationship of Vaccine Uptake and COVID-19 Infections Among Nursing Home Staff and Residents in Missouri: A Measure of Risk by Community Mobility. JOURNAL OF PUBLIC HEALTH MANAGEMENT AND PRACTICE 2024; 30:176-182. [PMID: 37831663 DOI: 10.1097/phh.0000000000001824] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/15/2023]
Abstract
BACKGROUND As the COVID-19 pandemic progressed across the United States, older adults living in nursing home (NH) facilities were disproportionately affected because of living in communal spaces with close proximity to others, age-related medical conditions, and constant contact with staff who may support multiple clients and facilities. While these populations are particularly at risk, there has been limited research focused on the management of the potential vectors of COVID-19 infection. METHODS Data from the Centers for Medicare & Medicaid Services (CMS) COVID-19 reporting system assessing weekly observations of COVID-19 case counts among NH residents and COVID-19 vaccination rates among NH staff and residents in the states of Missouri and Illinois (n = 877) from May 24, 2021, to August 28, 2021, were used. This ecological study, using results from the CMS COVID-19 reporting system, local COVID-19 rates, and NH-level demographic characteristics, conducted a zero inflation mode to determine the association between NH staff vaccine uptake and COVID-19 cases among NH residents. RESULTS Among the total 11 195 weekly observations within the NH facilities, zero cases of COVID-19 were reported during 10 683 (95%) of those weeks, supporting the use of a zero-inflated model. Results show that staff vaccination rates were significantly associated with a decrease in COVID-19 mortality. This study identified that for every percentage increase in staff vaccine coverage, the rate of COVID-19 among residents decreased by 2%. DISCUSSION These findings suggest that NH staff vaccination rates are significantly associated with the rate of COVID-19 outbreaks among NH residents. Community median income was associated with an increased likelihood of infection. Future research that explores associations with employment benefits and staff mobility, particularly in vulnerable populations, should be implemented in future vaccination strategic planning.
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Affiliation(s)
- Stephen Scroggins
- Department of Behavioral Science and Health Equity, College for Public Health and Social Justice (Drs Scroggins and Shacham, and Ms Little and Mr Okala), Saint Louis University, St Louis, Missouri; and Department of Psychiatry, Washington University School of Medicine, St Louis, Missouri (Dr Ellis)
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22
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Morand J, Yip S, Velegrakis Y, Lattanzi G, Potestio R, Tubiana L. Quality assessment and community detection methods for anonymized mobility data in the Italian Covid context. Sci Rep 2024; 14:4636. [PMID: 38409411 PMCID: PMC10897296 DOI: 10.1038/s41598-024-54878-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2023] [Accepted: 02/17/2024] [Indexed: 02/28/2024] Open
Abstract
We discuss how to assess the reliability of partial, anonymized mobility data and compare two different methods to identify spatial communities based on movements: Greedy Modularity Clustering (GMC) and the novel Critical Variable Selection (CVS). These capture different aspects of mobility: direct population fluxes (GMC) and the probability for individuals to move between two nodes (CVS). As a test case, we consider movements of Italians before and during the SARS-Cov2 pandemic, using Facebook users' data and publicly available information from the Italian National Institute of Statistics (Istat) to construct daily mobility networks at the interprovincial level. Using the Perron-Frobenius (PF) theorem, we show how the mean stochastic network has a stationary population density state comparable with data from Istat, and how this ceases to be the case if even a moderate amount of pruning is applied to the network. We then identify the first two national lockdowns through temporal clustering of the mobility networks, define two representative graphs for the lockdown and non-lockdown conditions and perform optimal spatial community identification on both graphs using the GMC and CVS approaches. Despite the fundamental differences in the methods, the variation of information (VI) between them assesses that they return similar partitions of the Italian provincial networks in both situations. The information provided can be used to inform policy, for example, to define an optimal scale for lockdown measures. Our approach is general and can be applied to other countries or geographical scales.
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Affiliation(s)
- Jules Morand
- University of Trento, via Sommarive 14, 38123, Trento, Italy.
- INFN-TIFPA, Trento Institute for Fundamental Physics and Applications, 38123, Trento, Italy.
| | - Shoichi Yip
- University of Trento, via Sommarive 14, 38123, Trento, Italy
| | - Yannis Velegrakis
- University of Trento, via Sommarive 14, 38123, Trento, Italy
- Utrecht University, Princetonplein 5, 3584 CC, Utrecht, The Netherlands
| | - Gianluca Lattanzi
- University of Trento, via Sommarive 14, 38123, Trento, Italy
- INFN-TIFPA, Trento Institute for Fundamental Physics and Applications, 38123, Trento, Italy
| | - Raffaello Potestio
- University of Trento, via Sommarive 14, 38123, Trento, Italy
- INFN-TIFPA, Trento Institute for Fundamental Physics and Applications, 38123, Trento, Italy
| | - Luca Tubiana
- University of Trento, via Sommarive 14, 38123, Trento, Italy
- INFN-TIFPA, Trento Institute for Fundamental Physics and Applications, 38123, Trento, Italy
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23
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Chang N, Tsai YC, Chen WJ, Lo CC, Chang HH. COVID-19 control measures unexpectedly increased the duration of stay at High Speed Rail stations during the first community outbreak in Taiwan. BMC Public Health 2024; 24:551. [PMID: 38388363 PMCID: PMC10882884 DOI: 10.1186/s12889-024-17964-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2023] [Accepted: 02/02/2024] [Indexed: 02/24/2024] Open
Abstract
During the COVID-19 pandemic, Taiwan has implemented strict border controls and community spread prevention measures. As part of these efforts, the government also implemented measures for public transportation. In Taiwan, there are two primary public transportation systems: Taiwan Railways (TR) is commonly utilized for local travel, while the Taiwan High-Speed Rail (THSR) is preferred for business trips and long-distance journeys due to its higher speed. In this study, we examined the impact of these disease prevention measures on the number of passengers and duration of stay in two major public transportation systems during the first community outbreak from April 29th to May 29th, 2021. Using data from a local telecommunications company, our study observed an expected decrease in the number of passengers after the cancellation of non-reserved seats at both TR and THSR stations across all 19 cities in the main island of Taiwan. Surprisingly, however, the duration of stay in some of the cities unexpectedly increased, especially at THSR stations. This unanticipated rise in the duration of stay has the potential to elevate contact probability among passengers and, consequently, the transmission rate. Our analysis shows that intervention policies may result in unforeseen outcomes, highlighting the crucial role of human mobility data as a real-time reference for policymakers. It enables them to monitor the impact of disease prevention measures and facilitates informed, data-driven decision-making.
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Affiliation(s)
- Ning Chang
- Institute of Systems Neuroscience, National Tsing Hua University, Hsinchu, Taiwan
| | - Yi-Chen Tsai
- Institute of Information Management, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Wei J Chen
- Centers of Genomic and Precision Medicine, National Taiwan University, Taipei, Taiwan
- Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei, Taiwan
- Center for Neuropsychiatric Research, National Health Research Institutes, Zhunan, Miaoli County, Taiwan
| | - Chung-Chuan Lo
- Institute of Systems Neuroscience, National Tsing Hua University, Hsinchu, Taiwan.
- Brain Research Center, National Tsing Hua University, Hsinchu, Taiwan.
| | - Hsiao-Han Chang
- Department of Life Science and Institute of Bioinformatics and Structural Biology, National Tsing Hua University, Hsinchu, Taiwan.
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24
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Aguilar-Ruiz JS, Ruiz R, Giráldez R. Advance Monitoring of COVID-19 Incidence Based on Taxi Mobility: The Infection Ratio Measure. Healthcare (Basel) 2024; 12:517. [PMID: 38470628 PMCID: PMC10930786 DOI: 10.3390/healthcare12050517] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2023] [Revised: 01/27/2024] [Accepted: 02/13/2024] [Indexed: 03/14/2024] Open
Abstract
The COVID-19 pandemic has had a profound impact on various aspects of our lives, affecting personal, occupational, economic, and social spheres. Much has been learned since the early 2020s, which will be very useful when the next pandemic emerges. In general, mobility and virus spread are strongly related. However, most studies analyze the impact of COVID-19 on mobility, but not much research has focused on analyzing the impact of mobility on virus transmission, especially from the point of view of monitoring virus incidence, which is extremely important for making sound decisions to control any epidemiological threat to public health. As a result of a thorough analysis of COVID-19 and mobility data, this work introduces a novel measure, the Infection Ratio (IR), which is not sensitive to underestimation of positive cases and is very effective in monitoring the pandemic's upward or downward evolution when it appears to be more stable, thus anticipating possible risk situations. For a bounded spatial context, we can infer that there is a significant threshold in the restriction of mobility that determines a change of trend in the number of infections that, if maintained for a minimum period, would notably increase the chances of keeping the spread of disease under control. Results show that IR is a reliable indicator of the intensity of infection, and an effective measure for early monitoring and decision making in smart cities.
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Affiliation(s)
- Jesus S. Aguilar-Ruiz
- School of Engineering, Pablo de Olavide University, 41013 Seville, Spain; (R.R.); (R.G.)
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25
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Xue J, Guo Y, Zhang M. Assessing the impact of isolation policies on epidemic dynamics through swarm entropy. Front Public Health 2024; 12:1338052. [PMID: 38389948 PMCID: PMC10881796 DOI: 10.3389/fpubh.2024.1338052] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2023] [Accepted: 01/23/2024] [Indexed: 02/24/2024] Open
Abstract
Isolation policies are an effective measure in epidemiological models for the prediction and prevention of infectious diseases. In this paper, we use a multi-agent modeling approach to construct an infectious disease model that considers the influence of isolation policies. The model analyzes the impact of isolation policies on various stages of epidemic from two perspectives: the external environment and agents behavior. It utilizes multiple variables to simulate the extent to which isolation policies influence the spread of the pandemic. Empirical evidence indicates that the progression of the epidemic is primarily driven by factors such as public willingness and regulatory intensity. The improved model, in comparison to traditional infectious disease models, offers greater flexibility and accuracy, addressing the need for frequent modifications in fundamental models within complex environments. Meanwhile, we introduce "swarm entropy" to evaluate infection intensity under various policies. By linking isolation policies with swarm entropy, considering population structure, we quantify the effectiveness of these isolation measures. It provides a novel approach for complex population simulations. These findings have facilitated the enhancement of control strategies and provided decision-makers with guidance in combating the transmission of infectious diseases.
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Affiliation(s)
- Junxiao Xue
- School of Cyber Science and Engineering, Zhengzhou University, Zhengzhou, China
- Zhejiang Lab, Research Institute of Artificial Intelligence, Hangzhou, China
- College of Computer Science and Technology (CCST), Zhejiang University (ZJU), Hangzhou, China
- School of Intelligent Science and Technology, Hangzhou Institute for Advanced Study of University of Chinese Academy of Sciences (UCAS), Hangzhou, China
| | - Yihang Guo
- School of Cyber Science and Engineering, Zhengzhou University, Zhengzhou, China
| | - Mingchuang Zhang
- National Digital Switching System Engineering and Technological R&D Center, People's Liberation Army Strategic Support Force Information Engineering University, Zhengzhou, China
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26
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Qiao J, Nishiura H. Public holidays increased the transmission of COVID-19 in Japan, 2020-2021: a mathematical modelling study. Epidemiol Health 2024; 46:e2024025. [PMID: 38317530 PMCID: PMC11099593 DOI: 10.4178/epih.e2024025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Accepted: 01/06/2024] [Indexed: 02/07/2024] Open
Abstract
OBJECTIVES Although the role of specific holidays in modifying transmission dynamics of infectious diseases has received some research attention, the epidemiological impact of public holidays on the transmission of coronavirus disease 2019 (COVID-19) remains unclear. METHODS To assess the extent of increased transmission frequency during public holidays, we collected COVID-19 incidence and mobility data in Hokkaido, Tokyo, Aichi, and Osaka from February 15, 2020 to September 30, 2021. Models linking the estimated effective reproduction number (Rt) with raw or adjusted mobility, public holidays, and the state of emergency declaration were developed. The best-fit model included public holidays as an essential input variable, and was used to calculate counterfactuals of Rt in the absence of holidays. RESULTS During public holidays, on average, Rt increased by 5.71%, 3.19%, 4.84%, and 24.82% in Hokkaido, Tokyo, Aichi, and Osaka, respectively, resulting in a total increase of 580 (95% confidence interval [CI], 213 to 954), 2,209 (95% CI, 1,230 to 3,201), 1,086 (95% CI, 478 to 1,686), and 5,211 (95% CI, 4,554 to 5,867) cases that were attributable to the impact of public holidays. CONCLUSIONS Public holidays intensified the transmission of COVID-19, highlighting the importance of considering public holidays in designing appropriate public health and social measures in the future.
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Affiliation(s)
- Jiaying Qiao
- School of Public Health, Kyoto University, Kyoto, Japan
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27
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Valdano E, Colombi D, Poletto C, Colizza V. Epidemic graph diagrams as analytics for epidemic control in the data-rich era. Nat Commun 2023; 14:8472. [PMID: 38123580 PMCID: PMC10733371 DOI: 10.1038/s41467-023-43856-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2023] [Accepted: 11/22/2023] [Indexed: 12/23/2023] Open
Abstract
COVID-19 highlighted modeling as a cornerstone of pandemic response. But it also revealed that current models may not fully exploit the high-resolution data on disease progression, epidemic surveillance and host behavior, now available. Take the epidemic threshold, which quantifies the spreading risk throughout epidemic emergence, mitigation, and control. Its use requires oversimplifying either disease or host contact dynamics. We introduce the epidemic graph diagrams to overcome this by computing the epidemic threshold directly from arbitrarily complex data on contacts, disease and interventions. A grammar of diagram operations allows to decompose, compare, simplify models with computational efficiency, extracting theoretical understanding. We use the diagrams to explain the emergence of resistant influenza variants in the 2007-2008 season, and demonstrate that neglecting non-infectious prodromic stages of sexually transmitted infections biases the predicted epidemic risk, compromising control. The diagrams are general, and improve our capacity to respond to present and future public health challenges.
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Affiliation(s)
- Eugenio Valdano
- Sorbonne Université, INSERM, Institut Pierre Louis d'Epidémiologie et de Santé Publique, F75012, Paris, France
| | | | - Chiara Poletto
- Department of Molecular Medicine, University of Padova, 35121, Padova, Italy
| | - Vittoria Colizza
- Sorbonne Université, INSERM, Institut Pierre Louis d'Epidémiologie et de Santé Publique, F75012, Paris, France.
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28
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Poudel N, Ngorsuraches S. A Preference-Based Value Assessment of the Fear of COVID-19 Contagion. Patient Prefer Adherence 2023; 17:3435-3448. [PMID: 38143944 PMCID: PMC10746924 DOI: 10.2147/ppa.s431148] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/20/2023] [Accepted: 12/06/2023] [Indexed: 12/26/2023] Open
Abstract
Purpose To assess the preference-based value of the fear of COVID-19 contagion. Patients and Methods We conducted a web-based, cross-sectional discrete choice experiment among 544 US adults. We used a Bayesian efficient design to generate choice sets. Each choice set comprised two hypothetical COVID-19 vaccine options characterized by seven attributes: chance of COVID-19 infection, chance of having severe symptoms from COVID-19 infection, vaccine protection duration, chance of mild to moderate adverse events from vaccination, chance of serious adverse events from vaccination, chance of future exposure to COVID-19 after vaccination, and out-of-pocket cost. We used mixed logit (ML) and latent class (LC) models to analyze data. Furthermore, we calculated the willingness-to-pay for eliminating the chance of future exposure to COVID-19, shedding light on the value attributed to the fear of contagion. Results The ML model demonstrated all attributes, including the chance of future exposure to COVID-19, were statistically significant. The participants were willing to pay approximately $13,046 to eliminate the chance of future exposure to COVID-19 or their fear of contagion when COVID-19 was still pandemic. The LC model unveiled two participant classes with distinct preference weights for the chance of future exposure to COVID-19 and out-of-pocket cost attributes. Nevertheless, the chance of future exposure to COVID-19 exposure held a significant degree of importance in both classes. Conclusion The chance of future exposure to COVID-19 exposure or fear of contagion was a significant element in the value assessment of COVID-19 vaccines. Further studies should be conducted to verify the value of fear of contagion and include it in the value assessment of healthcare technologies for infectious diseases.
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Affiliation(s)
- Nabin Poudel
- Department of Health Outcomes Research and Policy, Harrison College of Pharmacy, Auburn University, Auburn, AL, USA
| | - Surachat Ngorsuraches
- Department of Health Outcomes Research and Policy, Harrison College of Pharmacy, Auburn University, Auburn, AL, USA
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29
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Anzai A, Yamasaki S, Bleichrodt A, Chowell G, Nishida A, Nishiura H. Epidemiological impact of travel enhancement on the inter-prefectural importation dynamics of COVID-19 in Japan, 2020. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2023; 20:21499-21513. [PMID: 38124607 DOI: 10.3934/mbe.2023951] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/23/2023]
Abstract
Mobility restrictions were widely practiced to reduce contact with others and prevent the spatial spread of COVID-19 infection. Using inter-prefectural mobility and epidemiological data, a statistical model was devised to predict the number of imported cases in each Japanese prefecture. The number of imported cases crossing prefectural borders in 2020 was predicted using inter-prefectural mobility rates based on mobile phone data and prevalence estimates in the origin prefectures. The simplistic model was quantified using surveillance data of cases with an inter-prefectural travel history. Subsequently, simulations were carried out to understand how imported cases vary with the mobility rate and prevalence at the origin. Overall, the predicted number of imported cases qualitatively captured the observed number of imported cases over time. Although Hokkaido and Okinawa are the northernmost and the southernmost prefectures, respectively, they were sensitive to differing prevalence rate in Tokyo and Osaka and the mobility rate. Additionally, other prefectures were sensitive to mobility change, assuming that an increment in the mobility rate was seen in all prefectures. Our findings indicate the need to account for the weight of an inter-prefectural mobility network when implementing countermeasures to restrict human movement. If the mobility rates were maintained lower than the observed rates, then the number of imported cases could have been maintained at substantially lower levels than the observed, thus potentially preventing the unnecessary spatial spread of COVID-19 in late 2020.
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Affiliation(s)
- Asami Anzai
- Graduate School of Medicine, Kyoto University, Yoshidakonoecho, Sakyo-ku, Kyoto 606-8501, Japan
| | - Syudo Yamasaki
- Research Center for Social Science & Medicine, Tokyo Metropolitan Institute of Medical Science, Setagaya-ku, Tokyo, Japan
| | - Amanda Bleichrodt
- School of Public Health, Georgia State University, 140 Decatur St., Atlanta, GA 30303, USA
| | - Gerardo Chowell
- School of Public Health, Georgia State University, 140 Decatur St., Atlanta, GA 30303, USA
| | - Atsushi Nishida
- Research Center for Social Science & Medicine, Tokyo Metropolitan Institute of Medical Science, Setagaya-ku, Tokyo, Japan
- Tokyo Center for Infectious Disease Control and Prevention, Shinjuku-ku, Tokyo, Japan
| | - Hiroshi Nishiura
- Graduate School of Medicine, Kyoto University, Yoshidakonoecho, Sakyo-ku, Kyoto 606-8501, Japan
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30
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Maloney P, Kompaniyets L, Yusuf H, Bonilla L, Figueroa C, Garcia M. The effects of policy changes and human mobility on the COVID-19 epidemic in the Dominican Republic, 2020-2021. Prev Med Rep 2023; 36:102459. [PMID: 37840596 PMCID: PMC10568125 DOI: 10.1016/j.pmedr.2023.102459] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2023] [Revised: 10/03/2023] [Accepted: 10/04/2023] [Indexed: 10/17/2023] Open
Abstract
Recent advances in technology can be leveraged to enhance public health research and practice. This study aimed to assess the effects of mobility and policy changes on COVID-19 case growth and the effects of policy changes on mobility using data from Google Mobility Reports, information on public health policy, and COVID-19 testing results. Multiple bivariate regression analyses were conducted to address the study objectives. Policies designed to limit mobility led to decreases in mobility in public areas. These policies also decreased COVID-19 case growth. Conversely, policies that did not restrict mobility led to increases in mobility in public areas and led to increases in COVID-19 case growth. Mobility increases in public areas corresponded to increases in COVID-19 case growth, while concentration of mobility in residential areas corresponded to decreases in COVID-19 case growth. Overall, restrictive policies were effective in decreasing COVID-19 incidence in the Dominican Republic, while permissive policies led to increases in COVID-19 incidence.
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Affiliation(s)
- Patrick Maloney
- Centers for Disease Control and Prevention, Dominican Republic
| | - Lyudmyla Kompaniyets
- Centers for Disease Control and Prevention, Division of Nutrition, Physical Activity and Obesity, Obesity Prevention and Control Branch, Atlanta, GA, United States
| | - Hussain Yusuf
- Centers for Disease Control and Prevention, Division of Health Information and Surveillance, Partnerships and Evaluation Branch, Atlanta, GA, United States
| | - Luis Bonilla
- Centers for Disease Control and Prevention, Dominican Republic
| | - Carmen Figueroa
- Centers for Disease Control and Prevention, Dominican Republic
| | - Macarena Garcia
- Centers for Disease Control and Prevention, Dominican Republic
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31
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Xu S, Ferreira MAR, Porter EM, Franck CT. Bayesian model selection for generalized linear mixed models. Biometrics 2023; 79:3266-3278. [PMID: 37365985 DOI: 10.1111/biom.13896] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Accepted: 06/07/2023] [Indexed: 06/28/2023]
Abstract
We propose a Bayesian model selection approach for generalized linear mixed models (GLMMs). We consider covariance structures for the random effects that are widely used in areas such as longitudinal studies, genome-wide association studies, and spatial statistics. Since the random effects cannot be integrated out of GLMMs analytically, we approximate the integrated likelihood function using a pseudo-likelihood approach. Our Bayesian approach assumes a flat prior for the fixed effects and includes both approximate reference prior and half-Cauchy prior choices for the variances of random effects. Since the flat prior on the fixed effects is improper, we develop a fractional Bayes factor approach to obtain posterior probabilities of the several competing models. Simulation studies with Poisson GLMMs with spatial random effects and overdispersion random effects show that our approach performs favorably when compared to widely used competing Bayesian methods including deviance information criterion and Watanabe-Akaike information criterion. We illustrate the usefulness and flexibility of our approach with three case studies including a Poisson longitudinal model, a Poisson spatial model, and a logistic mixed model. Our proposed approach is implemented in the R package GLMMselect that is available on CRAN.
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Affiliation(s)
- Shuangshuang Xu
- Department of Statistics, Virginia Tech, Blacksburg, Virginia, USA
| | | | - Erica M Porter
- Department of Statistics, Virginia Tech, Blacksburg, Virginia, USA
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32
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Kwon H, Koylu C. Revealing associations between spatial time series trends of COVID-19 incidence and human mobility: an analysis of bidirectionality and spatiotemporal heterogeneity. Int J Health Geogr 2023; 22:33. [PMID: 38012610 PMCID: PMC10683178 DOI: 10.1186/s12942-023-00357-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2023] [Accepted: 11/21/2023] [Indexed: 11/29/2023] Open
Abstract
BACKGROUND Using human mobility as a proxy for social interaction, previous studies revealed bidirectional associations between COVID-19 incidence and human mobility. For example, while an increase in COVID-19 cases may affect mobility to decrease due to lockdowns or fear, conversely, an increase in mobility can potentially amplify social interactions, thereby contributing to an upsurge in COVID-19 cases. Nevertheless, these bidirectional relationships exhibit variations in their nature, evolve over time, and lack generalizability across different geographical contexts. Consequently, a systematic approach is required to detect functional, spatial, and temporal variations within the intricate relationship between disease incidence and mobility. METHODS We introduce a spatial time series workflow to investigate the bidirectional associations between human mobility and disease incidence, examining how these associations differ across geographic space and throughout different waves of a pandemic. By utilizing daily COVID-19 cases and mobility flows at the county level during three pandemic waves in the US, we conduct bidirectional Granger causality tests for each county and wave. Furthermore, we employ dynamic time warping to quantify the similarity between the trends of disease incidence and mobility, enabling us to map the spatial distribution of trends that are either similar or dissimilar. RESULTS Our analysis reveals significant bidirectional associations between COVID-19 incidence and mobility, and we develop a typology to explain the variations in these associations across waves and counties. Overall, COVID-19 incidence exerts a greater influence on mobility than vice versa, but the correlation between the two variables exhibits a stronger connection during the initial wave and weakens over time. Additionally, the relationship between COVID-19 incidence and mobility undergoes changes in direction and significance for certain counties across different waves. These shifts can be attributed to alterations in disease control measures and the presence of evolving confounding factors that differ both spatially and temporally. CONCLUSIONS This study provides insights into the spatial and temporal dynamics of the relationship between COVID-19 incidence and human mobility across different waves. Understanding these variations is crucial for informing the development of more targeted and effective healthcare policies and interventions, particularly at the city or county level where such policies must be implemented. Although we study the association between mobility and COVID-19 incidence, our workflow can be applied to investigate the associations between the time series trends of various infectious diseases and relevant contributing factors, which play a role in disease transmission.
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Affiliation(s)
- Hoeyun Kwon
- Department of Geographical and Sustainability Sciences, University of Iowa, Iowa City, IA, USA.
| | - Caglar Koylu
- Department of Geographical and Sustainability Sciences, University of Iowa, Iowa City, IA, USA
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33
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Drake JM, Handel A, Marty É, O’Dea EB, O’Sullivan T, Righi G, Tredennick AT. A data-driven semi-parametric model of SARS-CoV-2 transmission in the United States. PLoS Comput Biol 2023; 19:e1011610. [PMID: 37939201 PMCID: PMC10659176 DOI: 10.1371/journal.pcbi.1011610] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2023] [Revised: 11/20/2023] [Accepted: 10/17/2023] [Indexed: 11/10/2023] Open
Abstract
To support decision-making and policy for managing epidemics of emerging pathogens, we present a model for inference and scenario analysis of SARS-CoV-2 transmission in the USA. The stochastic SEIR-type model includes compartments for latent, asymptomatic, detected and undetected symptomatic individuals, and hospitalized cases, and features realistic interval distributions for presymptomatic and symptomatic periods, time varying rates of case detection, diagnosis, and mortality. The model accounts for the effects on transmission of human mobility using anonymized mobility data collected from cellular devices, and of difficult to quantify environmental and behavioral factors using a latent process. The baseline transmission rate is the product of a human mobility metric obtained from data and this fitted latent process. We fit the model to incident case and death reports for each state in the USA and Washington D.C., using likelihood Maximization by Iterated particle Filtering (MIF). Observations (daily case and death reports) are modeled as arising from a negative binomial reporting process. We estimate time-varying transmission rate, parameters of a sigmoidal time-varying fraction of hospitalized cases that result in death, extra-demographic process noise, two dispersion parameters of the observation process, and the initial sizes of the latent, asymptomatic, and symptomatic classes. In a retrospective analysis covering March-December 2020, we show how mobility and transmission strength became decoupled across two distinct phases of the pandemic. The decoupling demonstrates the need for flexible, semi-parametric approaches for modeling infectious disease dynamics in real-time.
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Affiliation(s)
- John M. Drake
- Odum School of Ecology, University of Georgia, Athens, Georgia, United States of America
- Center for the Ecology of Infectious Diseases, University of Georgia, Athens, Georgia, United States of America
| | - Andreas Handel
- Center for the Ecology of Infectious Diseases, University of Georgia, Athens, Georgia, United States of America
- College of Public Health, University of Georgia, Athens, Georgia, United States of America
| | - Éric Marty
- Odum School of Ecology, University of Georgia, Athens, Georgia, United States of America
- Center for the Ecology of Infectious Diseases, University of Georgia, Athens, Georgia, United States of America
| | - Eamon B. O’Dea
- Odum School of Ecology, University of Georgia, Athens, Georgia, United States of America
- Center for the Ecology of Infectious Diseases, University of Georgia, Athens, Georgia, United States of America
| | - Tierney O’Sullivan
- Odum School of Ecology, University of Georgia, Athens, Georgia, United States of America
- Center for the Ecology of Infectious Diseases, University of Georgia, Athens, Georgia, United States of America
| | - Giovanni Righi
- Odum School of Ecology, University of Georgia, Athens, Georgia, United States of America
- Center for the Ecology of Infectious Diseases, University of Georgia, Athens, Georgia, United States of America
| | - Andrew T. Tredennick
- Center for the Ecology of Infectious Diseases, University of Georgia, Athens, Georgia, United States of America
- Western EcoSystems Technology, Inc., Laramie, Wyoming, United States of America
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34
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Wagatsuma K. Association of Ambient Temperature and Absolute Humidity with the Effective Reproduction Number of COVID-19 in Japan. Pathogens 2023; 12:1307. [PMID: 38003771 PMCID: PMC10675148 DOI: 10.3390/pathogens12111307] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2023] [Revised: 10/28/2023] [Accepted: 10/30/2023] [Indexed: 11/26/2023] Open
Abstract
This study aimed to quantify the exposure-lag-response relationship between short-term changes in ambient temperature and absolute humidity and the transmission dynamics of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in Japan. The prefecture-specific daily time-series of newly confirmed cases, meteorological variables, retail and recreation mobility, and Government Stringency Index were collected for all 47 prefectures of Japan for the study period from 15 February 2020 to 15 October 2022. Generalized conditional Gamma regression models were formulated with distributed lag nonlinear models by adopting the case-time-series design to assess the independent and interactive effects of ambient temperature and absolute humidity on the relative risk (RR) of the time-varying effective reproductive number (Rt). With reference to 17.8 °C, the corresponding cumulative RRs (95% confidence interval) at a mean ambient temperatures of 5.1 °C and 27.9 °C were 1.027 (1.016-1.038) and 0.982 (0.974-0.989), respectively, whereas those at an absolute humidity of 4.2 m/g3 and 20.6 m/g3 were 1.026 (1.017-1.036) and 0.995 (0.985-1.006), respectively, with reference to 10.6 m/g3. Both extremely hot and humid conditions synergistically and slightly reduced the Rt. Our findings provide a better understanding of how meteorological drivers shape the complex heterogeneous dynamics of SARS-CoV-2 in Japan.
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Affiliation(s)
- Keita Wagatsuma
- Division of International Health (Public Health), Graduate School of Medical and Dental Sciences, Niigata University, Niigata 951-8510, Japan; ; Tel.: +81-25-227-2129
- Japan Society for the Promotion of Science, Tokyo 102-0083, Japan
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35
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Li J, Jia K, Zhao W, Yuan B, Liu Y. Natural and socio-environmental factors contribute to the transmissibility of COVID-19: evidence from an improved SEIR model. INTERNATIONAL JOURNAL OF BIOMETEOROLOGY 2023; 67:1789-1802. [PMID: 37561207 DOI: 10.1007/s00484-023-02539-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/06/2021] [Revised: 06/28/2023] [Accepted: 08/01/2023] [Indexed: 08/11/2023]
Abstract
COVID-19 has ravaged Brazil, and its spread showed spatial heterogeneity. Changes in the environment have been implicated as potential factors involved in COVID-19 transmission. However, considerable research efforts have not elucidated the risk of environmental factors on COVID-19 transmission from the perspective of infectious disease dynamics. The aim of this study is to model the influence of the environment on COVID-19 transmission and to analyze how the socio-ecological factors affecting the probability of virus transmission in 10 states dramatically shifted during the early stages of the epidemic in Brazil. First, this study used a Pearson correlation to analyze the interconnection between COVID-19 morbidity and socio-ecological factors and identified factors with significant correlations as the dominant factors affecting COVID-19 transmission. Then, the time-lag effect of dominant factors on the morbidity of COVID-19 was investigated by constructing a distributed lag nonlinear model and standard two-stage meta-analytic model, and the results were considered in the improved SEIR model. Lastly, a machine learning method was introduced to explore the nonlinear relationship between the environmental propagation probability and socio-ecological factors. By analyzing the impact of environmental factors on virus transmission, it can be found that population mobility directly caused by human activities had a greater impact on virus transmission than temperature and humidity. The heterogeneity of meteorological factors can be accounted for by the diverse climate patterns in Brazil. The improved SEIR model was adopted to explore the interconnection of COVID-19 transmission and the environment, which revealed a new strategy to probe the causal links between them.
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Affiliation(s)
- Jie Li
- State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing, 100875, China
| | - Kun Jia
- State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing, 100875, China.
| | - Wenwu Zhao
- Stake Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical Science, Beijing Normal University, Beijing, 100875, China
- Institute of Land Surface System and Sustainable Development, Faculty of Geographical Science, Beijing Normal University, Beijing, 100875, China
| | - Bo Yuan
- State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing, 100875, China
| | - Yanxu Liu
- Stake Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical Science, Beijing Normal University, Beijing, 100875, China
- Institute of Land Surface System and Sustainable Development, Faculty of Geographical Science, Beijing Normal University, Beijing, 100875, China
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36
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Erfani A, Frias-Martinez V. A fairness assessment of mobility-based COVID-19 case prediction models. PLoS One 2023; 18:e0292090. [PMID: 37851681 PMCID: PMC10584164 DOI: 10.1371/journal.pone.0292090] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2023] [Accepted: 09/12/2023] [Indexed: 10/20/2023] Open
Abstract
In light of the outbreak of COVID-19, analyzing and measuring human mobility has become increasingly important. A wide range of studies have explored spatiotemporal trends over time, examined associations with other variables, evaluated non-pharmacologic interventions (NPIs), and predicted or simulated COVID-19 spread using mobility data. Despite the benefits of publicly available mobility data, a key question remains unanswered: are models using mobility data performing equitably across demographic groups? We hypothesize that bias in the mobility data used to train the predictive models might lead to unfairly less accurate predictions for certain demographic groups. To test our hypothesis, we applied two mobility-based COVID infection prediction models at the county level in the United States using SafeGraph data, and correlated model performance with sociodemographic traits. Findings revealed that there is a systematic bias in models' performance toward certain demographic characteristics. Specifically, the models tend to favor large, highly educated, wealthy, young, and urban counties. We hypothesize that the mobility data currently used by many predictive models tends to capture less information about older, poorer, less educated and people from rural regions, which in turn negatively impacts the accuracy of the COVID-19 prediction in these areas. Ultimately, this study points to the need of improved data collection and sampling approaches that allow for an accurate representation of the mobility patterns across demographic groups.
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Affiliation(s)
- Abdolmajid Erfani
- Department of Civil, Environmental, and Geospatial Engineering, Michigan Technological University, Houghton, MI, United States of America
| | - Vanessa Frias-Martinez
- College of Information Studies, University of Maryland, College Park, MD, United States of America
- University of Maryland Institute for Advanced Computer Studies, University of Maryland, College Park, MD, United States of America
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37
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Bhatia S, Parag KV, Wardle J, Nash RK, Imai N, Elsland SLV, Lassmann B, Brownstein JS, Desai A, Herringer M, Sewalk K, Loeb SC, Ramatowski J, Cuomo-Dannenburg G, Jauneikaite E, Unwin HJT, Riley S, Ferguson N, Donnelly CA, Cori A, Nouvellet P. Retrospective evaluation of real-time estimates of global COVID-19 transmission trends and mortality forecasts. PLoS One 2023; 18:e0286199. [PMID: 37851661 PMCID: PMC10584190 DOI: 10.1371/journal.pone.0286199] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2022] [Accepted: 05/11/2023] [Indexed: 10/20/2023] Open
Abstract
Since 8th March 2020 up to the time of writing, we have been producing near real-time weekly estimates of SARS-CoV-2 transmissibility and forecasts of deaths due to COVID-19 for all countries with evidence of sustained transmission, shared online. We also developed a novel heuristic to combine weekly estimates of transmissibility to produce forecasts over a 4-week horizon. Here we present a retrospective evaluation of the forecasts produced between 8th March to 29th November 2020 for 81 countries. We evaluated the robustness of the forecasts produced in real-time using relative error, coverage probability, and comparisons with null models. During the 39-week period covered by this study, both the short- and medium-term forecasts captured well the epidemic trajectory across different waves of COVID-19 infections with small relative errors over the forecast horizon. The model was well calibrated with 56.3% and 45.6% of the observations lying in the 50% Credible Interval in 1-week and 4-week ahead forecasts respectively. The retrospective evaluation of our models shows that simple transmission models calibrated using routine disease surveillance data can reliably capture the epidemic trajectory in multiple countries. The medium-term forecasts can be used in conjunction with the short-term forecasts of COVID-19 mortality as a useful planning tool as countries continue to relax public health measures.
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Affiliation(s)
- Sangeeta Bhatia
- MRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, London, United Kingdom
- NIHR Health Protection Research Unit in Modelling and Health Economics, Modelling & Economics Unit, UK Health Security Agency, London, United Kingdom
| | - Kris V. Parag
- MRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, London, United Kingdom
| | - Jack Wardle
- MRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, London, United Kingdom
| | - Rebecca K. Nash
- MRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, London, United Kingdom
| | - Natsuko Imai
- MRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, London, United Kingdom
| | - Sabine L. Van Elsland
- MRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, London, United Kingdom
| | - Britta Lassmann
- ProMED-mail, International Society for Infectious Diseases, Brookline, MA, United States of America
| | - John S. Brownstein
- Boston Children’s Hospital, Computational Epidemiology Lab, Boston, MA, United States of America
| | - Angel Desai
- ProMED-mail, International Society for Infectious Diseases, Brookline, MA, United States of America
- Division of Infectious Diseases, Department of Internal Medicine, University of California Davis, Sacramento, California, United States of America
| | - Mark Herringer
- Healthsites.io, The Global Healthsites Mapping Project, London, United Kingdom
| | - Kara Sewalk
- Boston Children’s Hospital, Computational Epidemiology Lab, Boston, MA, United States of America
| | - Sarah Claire Loeb
- ProMED-mail, International Society for Infectious Diseases, Brookline, MA, United States of America
| | - John Ramatowski
- ProMED-mail, International Society for Infectious Diseases, Brookline, MA, United States of America
| | - Gina Cuomo-Dannenburg
- MRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, London, United Kingdom
| | - Elita Jauneikaite
- MRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, London, United Kingdom
| | - H. Juliette T. Unwin
- MRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, London, United Kingdom
| | - Steven Riley
- MRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, London, United Kingdom
| | - Neil Ferguson
- MRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, London, United Kingdom
| | - Christl A. Donnelly
- MRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, London, United Kingdom
- Department of Statistics, University of Oxford, Oxford, United Kingdom
| | - Anne Cori
- MRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, London, United Kingdom
| | - Pierre Nouvellet
- MRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, London, United Kingdom
- School of Life Sciences, University of Sussex, Brighton, United Kingdom
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38
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Zhang J, Tan S, Peng C, Xu X, Wang M, Lu W, Wu Y, Sai B, Cai M, Kummer AG, Chen Z, Zou J, Li W, Zheng W, Liang Y, Zhao Y, Vespignani A, Ajelli M, Lu X, Yu H. Heterogeneous changes in mobility in response to the SARS-CoV-2 Omicron BA.2 outbreak in Shanghai. Proc Natl Acad Sci U S A 2023; 120:e2306710120. [PMID: 37824525 PMCID: PMC10589641 DOI: 10.1073/pnas.2306710120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2023] [Accepted: 09/12/2023] [Indexed: 10/14/2023] Open
Abstract
The coronavirus disease 2019 (COVID-19) pandemic and the measures taken by authorities to control its spread have altered human behavior and mobility patterns in an unprecedented way. However, it remains unclear whether the population response to a COVID-19 outbreak varies within a city or among demographic groups. Here, we utilized passively recorded cellular signaling data at a spatial resolution of 1 km × 1 km for over 5 million users and epidemiological surveillance data collected during the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Omicron BA.2 outbreak from February to June 2022 in Shanghai, China, to investigate the heterogeneous response of different segments of the population at the within-city level and examine its relationship with the actual risk of infection. Changes in behavior were spatially heterogenous within the city and population groups and associated with both the infection incidence and adopted interventions. We also found that males and individuals aged 30 to 59 y old traveled more frequently, traveled longer distances, and their communities were more connected; the same groups were also associated with the highest SARS-CoV-2 incidence. Our results highlight the heterogeneous behavioral change of the Shanghai population to the SARS-CoV-2 Omicron BA.2 outbreak and the effect of heterogenous behavior on the spread of COVID-19, both spatially and demographically. These findings could be instrumental for the design of targeted interventions for the control and mitigation of future outbreaks of COVID-19, and, more broadly, of respiratory pathogens.
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Affiliation(s)
- Juanjuan Zhang
- Department of Epidemiology, School of Public Health, Key Laboratory of Public Health Safety, Ministry of Education, Fudan University, Shanghai200032, China
| | - Suoyi Tan
- College of Systems Engineering, National University of Defense Technology, Changsha410073, China
| | - Cheng Peng
- Department of Epidemiology, School of Public Health, Key Laboratory of Public Health Safety, Ministry of Education, Fudan University, Shanghai200032, China
| | - Xiangyanyu Xu
- Department of Epidemiology, School of Public Health, Key Laboratory of Public Health Safety, Ministry of Education, Fudan University, Shanghai200032, China
| | - Mengning Wang
- College of Systems Engineering, National University of Defense Technology, Changsha410073, China
| | - Wanying Lu
- Department of Epidemiology, School of Public Health, Key Laboratory of Public Health Safety, Ministry of Education, Fudan University, Shanghai200032, China
| | - Yanpeng Wu
- Department of Epidemiology, School of Public Health, Key Laboratory of Public Health Safety, Ministry of Education, Fudan University, Shanghai200032, China
| | - Bin Sai
- College of Systems Engineering, National University of Defense Technology, Changsha410073, China
| | - Mengsi Cai
- College of Systems Engineering, National University of Defense Technology, Changsha410073, China
| | - Allisandra G. Kummer
- Laboratory for Computational Epidemiology and Public Health, Department of Epidemiology and Biostatistics, Indiana University School of Public Health, Bloomington, IN47405
| | - Zhiyuan Chen
- Department of Epidemiology, School of Public Health, Key Laboratory of Public Health Safety, Ministry of Education, Fudan University, Shanghai200032, China
| | - Junyi Zou
- Department of Epidemiology, School of Public Health, Key Laboratory of Public Health Safety, Ministry of Education, Fudan University, Shanghai200032, China
| | - Wenxin Li
- Department of Epidemiology, School of Public Health, Key Laboratory of Public Health Safety, Ministry of Education, Fudan University, Shanghai200032, China
| | - Wen Zheng
- Department of Epidemiology, School of Public Health, Key Laboratory of Public Health Safety, Ministry of Education, Fudan University, Shanghai200032, China
| | - Yuxia Liang
- Department of Epidemiology, School of Public Health, Key Laboratory of Public Health Safety, Ministry of Education, Fudan University, Shanghai200032, China
| | - Yuchen Zhao
- Department of Epidemiology, School of Public Health, Key Laboratory of Public Health Safety, Ministry of Education, Fudan University, Shanghai200032, China
| | - Alessandro Vespignani
- Laboratory for the Modeling of Biological and Socio-technical Systems, Network Science Institute, Northeastern University, Boston, MA02115
| | - Marco Ajelli
- Laboratory for Computational Epidemiology and Public Health, Department of Epidemiology and Biostatistics, Indiana University School of Public Health, Bloomington, IN47405
| | - Xin Lu
- College of Systems Engineering, National University of Defense Technology, Changsha410073, China
| | - Hongjie Yu
- Department of Epidemiology, School of Public Health, Key Laboratory of Public Health Safety, Ministry of Education, Fudan University, Shanghai200032, China
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Jiang WM, Wen TH, Huang YC, Chiou HY, Chen WJ, Hsiung CA, Sytwu HK, Tsou HH. Interregional mobility in different age groups is associated with COVID-19 transmission in the Taipei metropolitan area, Taiwan. Sci Rep 2023; 13:17285. [PMID: 37828352 PMCID: PMC10570333 DOI: 10.1038/s41598-023-44474-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2022] [Accepted: 10/09/2023] [Indexed: 10/14/2023] Open
Abstract
Before vaccines were introduced, mobility restriction was one of the primary control measures in the early stage of the coronavirus disease 2019 (COVID-19) pandemic. Because different age groups face disproportionate health risks, differences in their mobility changes affect the effectiveness of pandemic control measures. This study aimed to investigate the relationship between multiscale mobility patterns in different age groups and COVID-19 transmission before and after control measures implementation. Data on daily confirmed case numbers, anonymized mobile phone data, and 38 socioeconomic factors were used to construct negative binomial regression models of these relationships in the Taipei metropolitan area in May 2021. To avoid overfitting, the socioeconomic factor dimensions were reduced by principal component analysis. The results showed that inter-district mobility was a greater promoter of COVID-19 transmission than was intra-district mobility (coefficients: pre-alert, 0.52 and 0.43; post-alert, 0.41 and 0.36, respectively). Moreover, both the inter-district mobility of people aged 15-59 and ≥ 60 years were significantly related to the number of confirmed cases (coefficients: pre-alert, 0.82 and 1.05; post-alert, 0.48 and 0.66, respectively). The results can help agencies worldwide formulate public health responses to emerging infectious diseases.
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Affiliation(s)
- Wei-Ming Jiang
- Institute of Population Health Sciences, National Health Research Institutes, 35 Keyan Road, Zhunan, 350, Miaoli County, Taiwan
| | - Tzai-Hung Wen
- Department of Geography, National Taiwan University, Taipei, Taiwan
| | - Ying-Chi Huang
- National Institute of Infectious Diseases and Vaccinology, National Health Research Institutes, Zhunan, Miaoli County, Taiwan
| | - Hung-Yi Chiou
- Institute of Population Health Sciences, National Health Research Institutes, 35 Keyan Road, Zhunan, 350, Miaoli County, Taiwan
- School of Public Health, College of Public Health, Taipei Medical University, Taipei, Taiwan
- Master's Program in Applied Epidemiology, College of Public Health, Taipei Medical University, Taipei, Taiwan
| | - Wei J Chen
- Center for Neuropsychiatric Research, National Health Research Institutes, Zhunan, Miaoli County, Taiwan
- Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei, Taiwan
| | - Chao A Hsiung
- Institute of Population Health Sciences, National Health Research Institutes, 35 Keyan Road, Zhunan, 350, Miaoli County, Taiwan
| | - Huey-Kang Sytwu
- National Institute of Infectious Diseases and Vaccinology, National Health Research Institutes, Zhunan, Miaoli County, Taiwan
| | - Hsiao-Hui Tsou
- Institute of Population Health Sciences, National Health Research Institutes, 35 Keyan Road, Zhunan, 350, Miaoli County, Taiwan.
- Graduate Institute of Biostatistics, College of Public Health, China Medical University, Taichung, Taiwan.
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40
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Murphy C, Lim WW, Mills C, Wong JY, Chen D, Xie Y, Li M, Gould S, Xin H, Cheung JK, Bhatt S, Cowling BJ, Donnelly CA. Effectiveness of social distancing measures and lockdowns for reducing transmission of COVID-19 in non-healthcare, community-based settings. PHILOSOPHICAL TRANSACTIONS. SERIES A, MATHEMATICAL, PHYSICAL, AND ENGINEERING SCIENCES 2023; 381:20230132. [PMID: 37611629 PMCID: PMC10446910 DOI: 10.1098/rsta.2023.0132] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/08/2023] [Accepted: 05/23/2023] [Indexed: 08/25/2023]
Abstract
Social distancing measures (SDMs) are community-level interventions that aim to reduce person-to-person contacts in the community. SDMs were a major part of the responses first to contain, then to mitigate, the spread of SARS-CoV-2 in the community. Common SDMs included limiting the size of gatherings, closing schools and/or workplaces, implementing work-from-home arrangements, or more stringent restrictions such as lockdowns. This systematic review summarized the evidence for the effectiveness of nine SDMs. Almost all of the studies included were observational in nature, which meant that there were intrinsic risks of bias that could have been avoided were conditions randomly assigned to study participants. There were no instances where only one form of SDM had been in place in a particular setting during the study period, making it challenging to estimate the separate effect of each intervention. The more stringent SDMs such as stay-at-home orders, restrictions on mass gatherings and closures were estimated to be most effective at reducing SARS-CoV-2 transmission. Most studies included in this review suggested that combinations of SDMs successfully slowed or even stopped SARS-CoV-2 transmission in the community. However, individual effects and optimal combinations of interventions, as well as the optimal timing for particular measures, require further investigation. This article is part of the theme issue 'The effectiveness of non-pharmaceutical interventions on the COVID-19 pandemic: the evidence'.
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Affiliation(s)
- Caitriona Murphy
- World Health Organization Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, People's Republic of China
| | - Wey Wen Lim
- World Health Organization Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, People's Republic of China
| | - Cathal Mills
- Department of Statistics, University of Oxford, Oxford, UK
| | - Jessica Y. Wong
- World Health Organization Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, People's Republic of China
| | - Dongxuan Chen
- World Health Organization Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, People's Republic of China
- Laboratory of Data Discovery for Health, Hong Kong Science and Technology Park, New Territories, Hong Kong, People's Republic of China
| | - Yanmy Xie
- World Health Organization Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, People's Republic of China
| | - Mingwei Li
- World Health Organization Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, People's Republic of China
- Laboratory of Data Discovery for Health, Hong Kong Science and Technology Park, New Territories, Hong Kong, People's Republic of China
| | - Susan Gould
- Department of Clinical Sciences, Liverpool School of Tropical Medicine, Liverpool, UK
- Tropical and Infectious Disease Unit, Liverpool University Hospitals NHS Foundation Trust, Liverpool, UK
| | - Hualei Xin
- World Health Organization Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, People's Republic of China
| | - Justin K. Cheung
- World Health Organization Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, People's Republic of China
| | - Samir Bhatt
- Section of Epidemiology, Department of Public Health, University of Copenhagen, Kobenhavn, Denmark
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, School of Public Health, Faculty of Medicine, Imperial College London, London, UK
| | - Benjamin J. Cowling
- World Health Organization Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, People's Republic of China
- Laboratory of Data Discovery for Health, Hong Kong Science and Technology Park, New Territories, Hong Kong, People's Republic of China
| | - Christl A. Donnelly
- Department of Statistics, University of Oxford, Oxford, UK
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, School of Public Health, Faculty of Medicine, Imperial College London, London, UK
- Pandemic Sciences Institute, University of Oxford, Oxford, UK
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41
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Robsky KO, Tram KH, Dowdy DW, Zelner J. Methods for measuring short-term geographical mobility used in infectious disease research: a scoping review protocol. BMJ Open 2023; 13:e072439. [PMID: 37793932 PMCID: PMC10551932 DOI: 10.1136/bmjopen-2023-072439] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/03/2023] [Accepted: 08/30/2023] [Indexed: 10/06/2023] Open
Abstract
INTRODUCTION Geographical mobility, the movement of individuals or populations, may increase an individual's risk of acquiring or transmitting infectious diseases, including HIV, tuberculosis, malaria and COVID-19. Many studies have collected information on short-term mobility through self-reported travel histories or using GPS trackers, but there has been no consistent conceptualisation and operationalisation of such geographical mobility in global health research. This protocol aims to describe and synthesise different approaches to measuring short-term mobility. METHODS AND ANALYSIS We will search three databases (PubMed, Embase and Global Health) for peer-reviewed articles. After removing duplicates, two reviewers will first screen the titles and abstracts and then proceed to full-text screening. We will include studies that measure mobility at the individual level in the context of infectious diseases, including clinical trials, epidemiological studies and analyses of register data. Additional articles for inclusion may be identified through review of references in selected papers. We will summarise the method of data collection (GPS trackers, cellphones, retrospective self-report, travel journal, etc) and the specific measures used (overnight travel, having a secondary residence, travel outside of district, etc). ETHICS AND DISSEMINATION This study consists of reviewing and abstracting existing data from publicly available materials, and therefore does not require ethical approval. The results of this study will be submitted for peer reviewed publication and may be presented at a relevant global health conference.
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Affiliation(s)
- Katherine O Robsky
- Center for Global Health Practice and Impact, Georgetown University, Washington, District of Columbia, USA
| | - Khai Hoan Tram
- Department of Medicine, University of Washington, Seattle, Washington, USA
| | - David W Dowdy
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA
- Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Jon Zelner
- Department of Epidemiology, University of Michigan, Ann Arbor, Michigan, USA
- Center for Social Epidemiology and Population Health, University of Michigan, Ann Arbor, Michigan, USA
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42
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Delussu F, Tizzoni M, Gauvin L. The limits of human mobility traces to predict the spread of COVID-19: A transfer entropy approach. PNAS NEXUS 2023; 2:pgad302. [PMID: 37811338 PMCID: PMC10558401 DOI: 10.1093/pnasnexus/pgad302] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/17/2023] [Accepted: 08/17/2023] [Indexed: 10/10/2023]
Abstract
Mobile phone data have been widely used to model the spread of COVID-19; however, quantifying and comparing their predictive value across different settings is challenging. Their quality is affected by various factors and their relationship with epidemiological indicators varies over time. Here, we adopt a model-free approach based on transfer entropy to quantify the relationship between mobile phone-derived mobility metrics and COVID-19 cases and deaths in more than 200 European subnational regions. Using multiple data sources over a one-year period, we found that past knowledge of mobility does not systematically provide statistically significant information on COVID-19 spread. Our approach allows us to determine the best metric for predicting disease incidence in a particular location, at different spatial scales. Additionally, we identify geographic and demographic factors, such as users' coverage and commuting patterns, that explain the (non)observed relationship between mobility and epidemic patterns. Our work provides epidemiologists and public health officials with a general-not limited to COVID-19-framework to evaluate the usefulness of human mobility data in responding to epidemics.
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Affiliation(s)
- Federico Delussu
- ISI Foundation, via Chisola 5, 10126 Torino, Italy
- Department of Applied Mathematics and Computer Science, DTU, Richard Petersens Plads, DK-2800 Copenhagen, Denmark
| | - Michele Tizzoni
- ISI Foundation, via Chisola 5, 10126 Torino, Italy
- Department of Sociology and Social Research, University of Trento, via Verdi 26, I-38122 Trento, Italy
| | - Laetitia Gauvin
- ISI Foundation, via Chisola 5, 10126 Torino, Italy
- UMR 215 PRODIG, Institute for Research on Sustainable Development - IRD, 5 cours des Humanités, F-93 322 Aubervilliers Cedex, France
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43
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Schindler DJ, Clarke J, Barahona M. Multiscale mobility patterns and the restriction of human movement. ROYAL SOCIETY OPEN SCIENCE 2023; 10:230405. [PMID: 37830024 PMCID: PMC10565406 DOI: 10.1098/rsos.230405] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Accepted: 09/18/2023] [Indexed: 10/14/2023]
Abstract
From the perspective of human mobility, the COVID-19 pandemic constituted a natural experiment of enormous reach in space and time. Here, we analyse the inherent multiple scales of human mobility using Facebook Movement maps collected before and during the first UK lockdown. Firstly, we obtain the pre-lockdown UK mobility graph and employ multiscale community detection to extract, in an unsupervised manner, a set of robust partitions into flow communities at different levels of coarseness. The partitions so obtained capture intrinsic mobility scales with better coverage than nomenclature of territorial units for statistics (NUTS) regions, which suffer from mismatches between human mobility and administrative divisions. Furthermore, the flow communities in the fine-scale partition not only match well the UK travel to work areas but also capture mobility patterns beyond commuting to work. We also examine the evolution of mobility under lockdown and show that mobility first reverted towards fine-scale flow communities already found in the pre-lockdown data, and then expanded back towards coarser flow communities as restrictions were lifted. The improved coverage induced by lockdown is well captured by a linear decay shock model, which allows us to quantify regional differences in both the strength of the effect and the recovery time from the lockdown shock.
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Affiliation(s)
| | - Jonathan Clarke
- Department of Mathematics, Imperial College London, London SW7 2BX, UK
| | - Mauricio Barahona
- Department of Mathematics, Imperial College London, London SW7 2BX, UK
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44
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Feltham E, Forastiere L, Alexander M, Christakis NA. Mass gatherings for political expression had no discernible association with the local course of the COVID-19 pandemic in the USA in 2020 and 2021. Nat Hum Behav 2023; 7:1708-1728. [PMID: 37524931 DOI: 10.1038/s41562-023-01654-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2022] [Accepted: 06/14/2023] [Indexed: 08/02/2023]
Abstract
Epidemic disease can spread during mass gatherings. We assessed the impact of a type of mass gathering about which comprehensive data were available on the local-area trajectory of the COVID-19 epidemic. Here we examined five types of political event in 2020 and 2021: the US primary elections, the US Senate special election in Georgia, the gubernatorial elections in New Jersey and Virginia, Donald Trump's political rallies and the Black Lives Matter protests. Our study period encompassed over 700 such mass gatherings during multiple phases of the pandemic. We used data from the 48 contiguous states, representing 3,108 counties, and we implemented a novel extension of a recently developed non-parametric, generalized difference-in-difference estimator with a (high-quality) matching procedure for panel data to estimate the average effect of the gatherings on local mortality and other outcomes. There were no statistically significant increases in cases, deaths or a measure of epidemic transmissibility (Rt) in a 40-day period following large-scale political activities. We estimated small and statistically non-significant effects, corresponding to an average difference of -0.0567 deaths (95% CI = -0.319, 0.162) and 8.275 cases (95% CI = -1.383, 20.7) on each day for counties that held mass gatherings for political expression compared to matched control counties. In sum, there is no statistical evidence of a material increase in local COVID-19 deaths, cases or transmissibility after mass gatherings for political expression during the first 2 years of the pandemic in the USA. This may relate to the specific manner in which such activities are typically conducted.
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Affiliation(s)
- Eric Feltham
- Yale Institute for Network Science, Yale University, New Haven, CT, USA.
- Department of Sociology, Yale University, New Haven, CT, USA.
| | - Laura Forastiere
- Yale Institute for Network Science, Yale University, New Haven, CT, USA
- Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA
| | - Marcus Alexander
- Yale Institute for Network Science, Yale University, New Haven, CT, USA
- Frank H. Netter MD School of Medicine, Quinnipiac University, North Haven, CT, USA
| | - Nicholas A Christakis
- Yale Institute for Network Science, Yale University, New Haven, CT, USA
- Department of Sociology, Yale University, New Haven, CT, USA
- Department of Statistics and Data Science, Yale University, New Haven, CT, USA
- Department of Medicine, Yale School of Medicine, New Haven, CT, USA
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45
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Bhatia S, Imai N, Watson OJ, Abbood A, Abdelmalik P, Cornelissen T, Ghozzi S, Lassmann B, Nagesh R, Ragonnet-Cronin ML, Schnitzler JC, Kraemer MU, Cauchemez S, Nouvellet P, Cori A. Lessons from COVID-19 for rescalable data collection. THE LANCET. INFECTIOUS DISEASES 2023; 23:e383-e388. [PMID: 37150186 PMCID: PMC10159580 DOI: 10.1016/s1473-3099(23)00121-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/05/2022] [Revised: 01/25/2023] [Accepted: 02/06/2023] [Indexed: 05/09/2023]
Abstract
Novel data and analyses have had an important role in informing the public health response to the COVID-19 pandemic. Existing surveillance systems were scaled up, and in some instances new systems were developed to meet the challenges posed by the magnitude of the pandemic. We describe the routine and novel data that were used to address urgent public health questions during the pandemic, underscore the challenges in sustainability and equity in data generation, and highlight key lessons learnt for designing scalable data collection systems to support decision making during a public health crisis. As countries emerge from the acute phase of the pandemic, COVID-19 surveillance systems are being scaled down. However, SARS-CoV-2 resurgence remains a threat to global health security; therefore, a minimal cost-effective system needs to remain active that can be rapidly scaled up if necessary. We propose that a retrospective evaluation to identify the cost-benefit profile of the various data streams collected during the pandemic should be on the scientific research agenda.
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Affiliation(s)
- Sangeeta Bhatia
- MRC Centre for Global Infectious Disease Analysis, Jameel Institute, School of Public Health, Imperial College London, London, UK; NIHR Health Protection Research Unit in Modelling and Health Economics, Imperial College London, UK Health Security Agency, and London School of Hygiene & Tropical Medicine, London, UK; Modelling and Economics Unit, UK Health Security Agency, London, UK
| | - Natsuko Imai
- MRC Centre for Global Infectious Disease Analysis, Jameel Institute, School of Public Health, Imperial College London, London, UK
| | - Oliver J Watson
- MRC Centre for Global Infectious Disease Analysis, Jameel Institute, School of Public Health, Imperial College London, London, UK; Faculty of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, London, UK
| | | | | | | | - Stéphane Ghozzi
- Helmholtz Centre for Infection Research, Braunschweig, Germany
| | - Britta Lassmann
- ProMED-mail, International Society for Infectious Diseases, Brookline, MA, USA
| | - Radhika Nagesh
- Blavatnik School of Government, University of Oxford, Oxford, UK
| | - Manon L Ragonnet-Cronin
- MRC Centre for Global Infectious Disease Analysis, Jameel Institute, School of Public Health, Imperial College London, London, UK; Department of Ecology & Evolution, University of Chicago, Chicago, IL, USA
| | | | - Moritz Ug Kraemer
- Department of Biology, University of Oxford, Oxford, UK; Pandemic Sciences Institute, University of Oxford, Oxford, UK
| | - Simon Cauchemez
- Mathematical Modelling of Infectious Diseases Unit, Institut Pasteur, Université Paris Cité, CNRS UMR2000, Paris, France
| | - Pierre Nouvellet
- MRC Centre for Global Infectious Disease Analysis, Jameel Institute, School of Public Health, Imperial College London, London, UK; School of Life Sciences, University of Sussex, Brighton, UK
| | - Anne Cori
- MRC Centre for Global Infectious Disease Analysis, Jameel Institute, School of Public Health, Imperial College London, London, UK; NIHR Health Protection Research Unit in Modelling and Health Economics, Imperial College London, UK Health Security Agency, and London School of Hygiene & Tropical Medicine, London, UK.
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46
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Basso F, Frez J, Hernández H, Leiva V, Pezoa R, Varas M. Crowding on public transport using smart card data during the COVID-19 pandemic: New methodology and case study in Chile. SUSTAINABLE CITIES AND SOCIETY 2023; 96:104712. [PMID: 37313370 PMCID: PMC10249364 DOI: 10.1016/j.scs.2023.104712] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/10/2022] [Revised: 06/03/2023] [Accepted: 06/03/2023] [Indexed: 06/15/2023]
Abstract
Most crowding measures in public transportation are usually aggregated at a service level. This type of aggregation does not help to analyze microscopic behavior such as exposure risk to viruses. To bridge such a gap, our paper proposes four novel crowding measures that might be well suited to proxy virus exposure risk at public transport. In addition, we conduct a case study in Santiago, Chile, using smart card data of the buses system to compute the proposed measures for three different and relevant periods of the COVID-19 pandemic: before, during, and after Santiago's lockdown. We find that the governmental policies diminished public transport crowding considerably for the lockdown phase. The average exposure time when social distancing is not possible passes from 6.39 min before lockdown to 0.03 min during the lockdown, while the average number of encountered persons passes from 43.33 to 5.89. We shed light on how the pandemic impacts differ across various population groups in society. Our findings suggest that poorer municipalities returned faster to crowding levels similar to those before the pandemic.
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Affiliation(s)
- Franco Basso
- Escuela de Ingeniería Industrial, Pontificia Universidad Católica de Valparaíso, Valparaíso, Chile
- Instituto Sistemas Complejos de Ingeniería (ISCI), Chile
| | - Jonathan Frez
- Escuela de Ingeniería Informática y Telecomunicaciones, Universidad Diego Portales, Santiago, Chile
| | - Hugo Hernández
- Escuela de Ingeniería Industrial, Pontificia Universidad Católica de Valparaíso, Valparaíso, Chile
| | - Víctor Leiva
- Escuela de Ingeniería Industrial, Pontificia Universidad Católica de Valparaíso, Valparaíso, Chile
| | - Raúl Pezoa
- Escuela de Ingeniería Industrial, Universidad Diego Portales, Santiago, Chile
| | - Mauricio Varas
- Centro de Investigación en Sustentabilidad y Gestión Estratégica de Recursos, Universidad del Desarrollo, Santiago, Chile
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47
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Zheng Y, Lin Y, Zhao L, Wu T, Jin D, Li Y. Spatial planning of urban communities via deep reinforcement learning. NATURE COMPUTATIONAL SCIENCE 2023; 3:748-762. [PMID: 38177774 DOI: 10.1038/s43588-023-00503-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/15/2023] [Accepted: 07/27/2023] [Indexed: 01/06/2024]
Abstract
Effective spatial planning of urban communities plays a critical role in the sustainable development of cities. Despite the convenience brought by geographic information systems and computer-aided design, determining the layout of land use and roads still heavily relies on human experts. Here we propose an artificial intelligence urban-planning model to generate spatial plans for urban communities. To overcome the difficulty of diverse and irregular urban geography, we construct a graph to describe the topology of cities in arbitrary forms and formulate urban planning as a sequential decision-making problem on the graph. To tackle the challenge of the vast solution space, we develop a reinforcement learning model based on graph neural networks. Experiments on both synthetic and real-world communities demonstrate that our computational model outperforms plans designed by human experts in objective metrics and that it can generate spatial plans responding to different circumstances and needs. We also propose a human-artificial intelligence collaborative workflow of urban planning, in which human designers can substantially benefit from our model to be more productive, generating more efficient spatial plans with much less time. Our method demonstrates the great potential of computational urban planning and paves the way for more explorations in leveraging computational methodologies to solve challenging real-world problems in urban science.
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Affiliation(s)
- Yu Zheng
- Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing, P. R. China
- Department of Electronic Engineering, Tsinghua University, Beijing, P. R. China
| | - Yuming Lin
- Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing, P. R. China
- Department of Electronic Engineering, Tsinghua University, Beijing, P. R. China
| | - Liang Zhao
- Department of Urban Planning and Design, Tsinghua University, Beijing, P. R. China
| | - Tinghai Wu
- Department of Urban Planning and Design, Tsinghua University, Beijing, P. R. China
| | - Depeng Jin
- Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing, P. R. China
- Department of Electronic Engineering, Tsinghua University, Beijing, P. R. China
| | - Yong Li
- Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing, P. R. China.
- Department of Electronic Engineering, Tsinghua University, Beijing, P. R. China.
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Palupi S, Chongsuvivatwong V, Surya A, Suyanto S, Kumwichar P. Cross-Risk Between Tuberculosis and COVID-19 in East Java Province, Indonesia: An Analysis of Tuberculosis and COVID-19 Surveillance Registry Period 2020-2022. Cureus 2023; 15:e44857. [PMID: 37692189 PMCID: PMC10485793 DOI: 10.7759/cureus.44857] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/07/2023] [Indexed: 09/12/2023] Open
Abstract
INTRODUCTION Tuberculosis (TB) and COVID-19 are highly transmissible diseases and pose a serious risk to public health. Unfortunately, information on cross-risk between the two diseases was still sparse. Our main objective was to estimate the excess risk among TB patients in getting COVID-19 infection and vice versa. METHODS The study design was a series of analyses of existing data from TB and COVID-19 registries in East Java Province, Indonesia. The study period was from January 2020 to June 2022. Case-by-case data for this study were obtained from the registration systems for TB and COVID-19 in separate databases. In comparing risk across different groups, adjusting for differences in risk factors that influence the outcome was essential. We overcame this problem by employing a standardized morbidity ratio. RESULTS Among 92,424 newly diagnosed TB patients, 1,326 were subsequently infected with COVID-19 during the study period, compared with 1,679 expected. The standardized morbidity ratio (95% confidence interval) was 72.61% (60.19%, 85.03%). Among 635,946 newly diagnosed COVID-19-infected patients, 987 subsequently got active TB during the study period against 1,679 expected. The standardized morbidity ratio (95% confidence interval) was 55.33% (49.24%, 61.42%). CONCLUSION There was no evidence of excess risk in either direction, the excess risk among TB patients in getting COVID-19 infection and vice versa.
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Affiliation(s)
- Satiti Palupi
- Department of Epidemiology, Prince of Songkla University, Hat Yai, THA
- Department of Communicable Disease, East Java Provincial Health Office, Surabaya, IDN
| | | | - Asik Surya
- Department of Direct Communicable Disease Prevention and Control, Ministry of Health Republic of Indonesia, Jakarta, IDN
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Melo ADC, Santos ICD, Trevisani AC, Rúbio KAJ, Cano AL, Caldart ET, Mendonça JA, Agostinis RO, Piau Junior R, Gazim ZC, Barbosa LN, Gonçalves DD. Relations Between Guardians and Domestic Animals During the Sars-Cov-2 Pandemic in Brazil. Disaster Med Public Health Prep 2023; 17:e466. [PMID: 37622310 DOI: 10.1017/dmp.2023.107] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/26/2023]
Abstract
OBJECTIVE The aim of this study was to evaluate how relationships between guardians and domestic animals were established and their possible effects during the COVID-19 pandemic period in Brazil. METHODS Data were collected by completing an online questionnaire. Throughout the national territory, 2002 people completed the questionnaire, while respecting social distancing from January 20, 2021, to March 20, 2021. The inclusion and exclusion criteria were 'to own pets' or 'those who had them during the period of quarantine' in Brazil. The data obtained were treated qualitatively through discourse analysis and content analysis, while the quantitative data were tabulated by the questionnaire application platform itself. RESULTS The results showed that 97% of the participants were affected by the coronavirus pandemic in Brazil and that of these, 95.5% stated that their pet was important to overcome and bear bad feelings during the period of social isolation. It was possible to observe through the participants' reports through the online questionnaire that the relationships with their pets were deepened during the pandemic period, and that these had an important role in overcoming bad emotions caused by social distancing. CONCLUSION It was concluded that due to these relationships, behaviors such as anxiety and sadness were reduced and the pets themselves also showed an increase in affective behaviors in relation to their guardians.
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Affiliation(s)
- Ailton da Cruz Melo
- Universidade Paranaense (UNIPAR), Praça Mascarenhas de Moraes, Umuarama, Paraná, Brasil
| | - Isabela Carvalho Dos Santos
- Programa de Pós-Graduação em Ciência Animal com Ênfase em Produtos Bioativos, Universidade Paranaense (UNIPAR), Praça Mascarenhas de Moraes, Umuarama, Paraná, Brasil
| | - Adriane Cordeiro Trevisani
- Programa de Pós-Graduação em Ciência Animal com Ênfase em Produtos Bioativos, Universidade Paranaense (UNIPAR), Praça Mascarenhas de Moraes, Umuarama, Paraná, Brasil
| | - Kariny Aparecida Jardim Rúbio
- Programa de Pós-Graduação em Ciência Animal com Ênfase em Produtos Bioativos, Universidade Paranaense (UNIPAR), Praça Mascarenhas de Moraes, Umuarama, Paraná, Brasil
| | - Ana Luisa Cano
- Universidade Paranaense (UNIPAR), Praça Mascarenhas de Moraes, Umuarama, Paraná, Brasil
| | - Eloiza Teles Caldart
- Departamento de Medicina Veterinária Preventiva, Universidade Estadual de Londrina, Rodovia, Londrina, Brasil
| | | | - Renata Olivotto Agostinis
- Programa de Pós-Graduação em Ciência Animal com Ênfase em Produtos Bioativos, Universidade Paranaense (UNIPAR), Praça Mascarenhas de Moraes, Umuarama, Paraná, Brasil
| | - Ranulfo Piau Junior
- Programa de Pós-Graduação em Ciência Animal com Ênfase em Produtos Bioativos, Universidade Paranaense (UNIPAR), Praça Mascarenhas de Moraes, Umuarama, Paraná, Brasil
| | - Zilda Cristiani Gazim
- Universidade Paranaense (UNIPAR), Praça Mascarenhas de Moraes, Umuarama, Paraná, Brasil
- Programa de Pós-Graduação em Ciência Animal com Ênfase em Produtos Bioativos, Universidade Paranaense (UNIPAR), Praça Mascarenhas de Moraes, Umuarama, Paraná, Brasil
| | - Lidiane Nunes Barbosa
- Universidade Paranaense (UNIPAR), Praça Mascarenhas de Moraes, Umuarama, Paraná, Brasil
- Programa de Pós-Graduação em Ciência Animal com Ênfase em Produtos Bioativos, Universidade Paranaense (UNIPAR), Praça Mascarenhas de Moraes, Umuarama, Paraná, Brasil
| | - Daniela Dib Gonçalves
- Universidade Paranaense (UNIPAR), Praça Mascarenhas de Moraes, Umuarama, Paraná, Brasil
- Programa de Pós-Graduação em Ciência Animal com Ênfase em Produtos Bioativos, Universidade Paranaense (UNIPAR), Praça Mascarenhas de Moraes, Umuarama, Paraná, Brasil
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Huang L, Wang D. Teacher Support, Academic Self-Efficacy, Student Engagement, and Academic Achievement in Emergency Online Learning. Behav Sci (Basel) 2023; 13:704. [PMID: 37753982 PMCID: PMC10525361 DOI: 10.3390/bs13090704] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Revised: 08/15/2023] [Accepted: 08/22/2023] [Indexed: 09/28/2023] Open
Abstract
With a sample of 651 university students experiencing emergency online learning during COVID-19, this study constructed a structural equation modelling to examine the effects of teacher support on students' academic achievement, with a particular focus on the mediating roles of academic self-efficacy and student engagement. The results show that teacher support had significant total influences on university students' academic achievement. Furthermore, academic self-efficacy and student engagement, respectively, mediated the effects of teacher support on students' academic achievement. In addition, academic self-efficacy and student engagement sequentially mediated the effects of teacher support on students' academic achievement. Research implications are also discussed.
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Affiliation(s)
- Liang Huang
- Department of Public Administration, Southeast University, Nanjing 211189, China
| | - Dongsheng Wang
- Faculty of Education, Northwest Normal University, Lanzhou 730070, China
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