1
|
Chowell G, Bleichrodt A, Dahal S, Tariq A, Roosa K, Hyman JM, Luo R. GrowthPredict: A toolbox and tutorial-based primer for fitting and forecasting growth trajectories using phenomenological growth models. Sci Rep 2024; 14:1630. [PMID: 38238407 PMCID: PMC10796326 DOI: 10.1038/s41598-024-51852-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2023] [Accepted: 01/10/2024] [Indexed: 01/22/2024] Open
Abstract
Simple dynamic modeling tools can help generate real-time short-term forecasts with quantified uncertainty of the trajectory of diverse growth processes unfolding in nature and society, including disease outbreaks. An easy-to-use and flexible toolbox for this purpose is lacking. This tutorial-based primer introduces and illustrates GrowthPredict, a user-friendly MATLAB toolbox for fitting and forecasting time-series trajectories using phenomenological dynamic growth models based on ordinary differential equations. This toolbox is accessible to a broad audience, including students training in mathematical biology, applied statistics, and infectious disease modeling, as well as researchers and policymakers who need to conduct short-term forecasts in real-time. The models included in the toolbox capture exponential and sub-exponential growth patterns that typically follow a rising pattern followed by a decline phase, a common feature of contagion processes. Models include the 1-parameter exponential growth model and the 2-parameter generalized-growth model, which have proven useful in characterizing and forecasting the ascending phase of epidemic outbreaks. It also includes the 2-parameter Gompertz model, the 3-parameter generalized logistic-growth model, and the 3-parameter Richards model, which have demonstrated competitive performance in forecasting single peak outbreaks. We provide detailed guidance on forecasting time-series trajectories and available software ( https://github.com/gchowell/forecasting_growthmodels ), including the full uncertainty distribution derived through parametric bootstrapping, which is needed to construct prediction intervals and evaluate their accuracy. Functions are available to assess forecasting performance across different models, estimation methods, error structures in the data, and forecasting horizons. The toolbox also includes functions to quantify forecasting performance using metrics that evaluate point and distributional forecasts, including the weighted interval score. This tutorial and toolbox can be broadly applied to characterizing and forecasting time-series data using simple phenomenological growth models. As a contagion process takes off, the tools presented in this tutorial can help create forecasts to guide policy regarding implementing control strategies and assess the impact of interventions. The toolbox functionality is demonstrated through various examples, including a tutorial video, and the examples use publicly available data on the monkeypox (mpox) epidemic in the USA.
Collapse
Affiliation(s)
- Gerardo Chowell
- Department of Population Health Sciences, School of Public Health, Georgia State University, Atlanta, GA, USA.
| | - Amanda Bleichrodt
- Department of Population Health Sciences, School of Public Health, Georgia State University, Atlanta, GA, USA
| | - Sushma Dahal
- Department of Population Health Sciences, School of Public Health, Georgia State University, Atlanta, GA, USA
| | - Amna Tariq
- School of Medicine, Stanford University, Stanford, CA, USA
| | - Kimberlyn Roosa
- National Institute for Mathematical and Biological Synthesis (NIMBioS), University of Tennessee, Knoxville, TN, USA
| | - James M Hyman
- Department of Mathematics, Center for Computational Science, Tulane University, New Orleans, LA, USA
| | - Ruiyan Luo
- Department of Population Health Sciences, School of Public Health, Georgia State University, Atlanta, GA, USA
| |
Collapse
|
2
|
Chowell G, Bleichrodt A, Dahal S, Tariq A, Roosa K, Hyman JM, Luo R. A MATLAB toolbox to fit and forecast growth trajectories using phenomenological growth models: Application to epidemic outbreaks. RESEARCH SQUARE 2023:rs.3.rs-2724940. [PMID: 37034746 PMCID: PMC10081381 DOI: 10.21203/rs.3.rs-2724940/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/26/2023]
Abstract
Background Simple dynamic modeling tools can be useful for generating real-time short-term forecasts with quantified uncertainty of the trajectory of diverse growth processes unfolding in nature and society, including disease outbreaks. An easy-to-use and flexible toolbox for this purpose is lacking. Results In this tutorial-based primer, we introduce and illustrate a user-friendly MATLAB toolbox for fitting and forecasting time-series trajectories using phenomenological dynamic growth models based on ordinary differential equations. This toolbox is accessible to various audiences, including students training in time-series forecasting, dynamic growth modeling, parameter estimation, parameter uncertainty and identifiability, model comparison, performance metrics, and forecast evaluation, as well as researchers and policymakers who need to conduct short-term forecasts in real-time. The models included in the toolbox capture exponential and sub-exponential growth patterns that typically follow a rising pattern followed by a decline phase, a common feature of contagion processes. Models include the 2-parameter generalized-growth model, which has proved useful to characterize and forecast the ascending phase of epidemic outbreaks, and the Gompertz model as well as the 3-parameter generalized logistic-growth model and the Richards model, which have demonstrated competitive performance in forecasting single peak outbreaks.The toolbox provides a tutorial for forecasting time-series trajectories that include the full uncertainty distribution, derived through parametric bootstrapping, which is needed to construct prediction intervals and evaluate their accuracy. Functions are available to assess forecasting performance across different models, estimation methods, error structures in the data, and forecasting horizons. The toolbox also includes functions to quantify forecasting performance using metrics that evaluate point and distributional forecasts, including the weighted interval score. Conclusions We have developed the first comprehensive toolbox to characterize and forecast time-series data using simple phenomenological growth models. As a contagion process takes off, the tools presented in this tutorial can facilitate policymaking to guide the implementation of control strategies and assess the impact of interventions. The toolbox functionality is demonstrated through various examples, including a tutorial video, and is illustrated using weekly data on the monkeypox epidemic in the USA.
Collapse
Affiliation(s)
- Gerardo Chowell
- Department of Population Health Sciences, School of Public Health, Georgia State University, Atlanta, GA, USA
| | - Amanda Bleichrodt
- Department of Population Health Sciences, School of Public Health, Georgia State University, Atlanta, GA, USA
| | - Sushma Dahal
- Department of Population Health Sciences, School of Public Health, Georgia State University, Atlanta, GA, USA
| | - Amna Tariq
- Stanford University, School of Medicine, CA, USA
| | - Kimberlyn Roosa
- National Institute for Mathematical and Biological Synthesis (NIMBioS), University of Tennessee, Knoxville, TN, USA
| | - James M Hyman
- Department of Mathematics, Center for Computational Science, Tulane University, New Orleans, LA, USA
| | - Ruiyan Luo
- Department of Population Health Sciences, School of Public Health, Georgia State University, Atlanta, GA, USA
| |
Collapse
|
3
|
Chowell G, Bleichrodt A, Dahal S, Tariq A, Roosa K, Hyman JM, Luo R. A MATLAB toolbox to fit and forecast growth trajectories using phenomenological growth models: Application to epidemic outbreaks. RESEARCH SQUARE 2023:rs.3.rs-2724940. [PMID: 37034746 PMCID: PMC10081381 DOI: 10.21203/rs.3.rs-2724940/v2] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
BACKGROUND Simple dynamic modeling tools can be useful for generating real-time short-term forecasts with quantified uncertainty of the trajectory of diverse growth processes unfolding in nature and society, including disease outbreaks. An easy-to-use and flexible toolbox for this purpose is lacking. RESULTS In this tutorial-based primer, we introduce and illustrate a user-friendly MATLAB toolbox for fitting and forecasting time-series trajectories using phenomenological dynamic growth models based on ordinary differential equations. This toolbox is accessible to various audiences, including students training in time-series forecasting, dynamic growth modeling, parameter estimation, parameter uncertainty and identifiability, model comparison, performance metrics, and forecast evaluation, as well as researchers and policymakers who need to conduct short-term forecasts in real-time. The models included in the toolbox capture exponential and sub-exponential growth patterns that typically follow a rising pattern followed by a decline phase, a common feature of contagion processes. Models include the 2-parameter generalized-growth model, which has proved useful to characterize and forecast the ascending phase of epidemic outbreaks, and the Gompertz model as well as the 3-parameter generalized logistic-growth model and the Richards model, which have demonstrated competitive performance in forecasting single peak outbreaks.The toolbox provides a tutorial for forecasting time-series trajectories that include the full uncertainty distribution, derived through parametric bootstrapping, which is needed to construct prediction intervals and evaluate their accuracy. Functions are available to assess forecasting performance across different models, estimation methods, error structures in the data, and forecasting horizons. The toolbox also includes functions to quantify forecasting performance using metrics that evaluate point and distributional forecasts, including the weighted interval score. CONCLUSIONS We have developed the first comprehensive toolbox to characterize and forecast time-series data using simple phenomenological growth models. As a contagion process takes off, the tools presented in this tutorial can facilitate policymaking to guide the implementation of control strategies and assess the impact of interventions. The toolbox functionality is demonstrated through various examples, including a tutorial video, and is illustrated using weekly data on the monkeypox epidemic in the USA.
Collapse
Affiliation(s)
- Gerardo Chowell
- Department of Population Health Sciences, School of Public Health, Georgia State University, Atlanta, GA, USA
| | - Amanda Bleichrodt
- Department of Population Health Sciences, School of Public Health, Georgia State University, Atlanta, GA, USA
| | - Sushma Dahal
- Department of Population Health Sciences, School of Public Health, Georgia State University, Atlanta, GA, USA
| | - Amna Tariq
- Stanford University, School of Medicine, CA, USA
| | - Kimberlyn Roosa
- National Institute for Mathematical and Biological Synthesis (NIMBioS), University of Tennessee, Knoxville, TN, USA
| | - James M Hyman
- Department of Mathematics, Center for Computational Science, Tulane University, New Orleans, LA, USA
| | - Ruiyan Luo
- Department of Population Health Sciences, School of Public Health, Georgia State University, Atlanta, GA, USA
| |
Collapse
|
4
|
Luebben G, González-Parra G, Cervantes B. Study of optimal vaccination strategies for early COVID-19 pandemic using an age-structured mathematical model: A case study of the USA. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2023; 20:10828-10865. [PMID: 37322963 PMCID: PMC11216547 DOI: 10.3934/mbe.2023481] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
In this paper we study different vaccination strategies that could have been implemented for the early COVID-19 pandemic. We use a demographic epidemiological mathematical model based on differential equations in order to investigate the efficacy of a variety of vaccination strategies under limited vaccine supply. We use the number of deaths as the metric to measure the efficacy of each of these strategies. Finding the optimal strategy for the vaccination programs is a complex problem due to the large number of variables that affect the outcomes. The constructed mathematical model takes into account demographic risk factors such as age, comorbidity status and social contacts of the population. We perform simulations to assess the performance of more than three million vaccination strategies which vary depending on the vaccine priority of each group. This study focuses on the scenario corresponding to the early vaccination period in the USA, but can be extended to other countries. The results of this study show the importance of designing an optimal vaccination strategy in order to save human lives. The problem is extremely complex due to the large amount of factors, high dimensionality and nonlinearities. We found that for low/moderate transmission rates the optimal strategy prioritizes high transmission groups, but for high transmission rates, the optimal strategy focuses on groups with high CFRs. The results provide valuable information for the design of optimal vaccination programs. Moreover, the results help to design scientific vaccination guidelines for future pandemics.
Collapse
Affiliation(s)
- Giulia Luebben
- Department of Mathematics, New Mexico Tech, New Mexico, 87801, USA
| | | | - Bishop Cervantes
- Department of Mathematics, New Mexico Tech, New Mexico, 87801, USA
| |
Collapse
|
5
|
Borg MG, Borg MA. A Trendline and Predictive Analysis of the First-Wave COVID-19 Infections in Malta. EPIDEMIOLOGIA (BASEL, SWITZERLAND) 2023; 4:33-50. [PMID: 36648777 PMCID: PMC9844502 DOI: 10.3390/epidemiologia4010003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Revised: 12/26/2022] [Accepted: 01/04/2023] [Indexed: 01/13/2023]
Abstract
Following the first COVID-19 infected cases, Malta rapidly imposed strict lockdown measures, including restrictions on international travel, together with national social distancing measures, such as prohibition of public gatherings and closure of workplaces. The study aimed to elucidate the effect of the intervention and relaxation of the social distancing measures upon the infection rate by means of a trendline analysis of the daily case data. In addition, the study derived a predictive model by fitting historical data of the SARS-CoV-2 positive cases within a two-parameter Weibull distribution, whilst incorporating swab-testing rates, to forecast the infection rate at minute computational expense. The trendline analysis portrayed the wave of infection to fit within a tri-phasic pattern, where the primary phase was imposed with social measure interventions. Following the relaxation of public measures, the two latter phases transpired, where the two peaks resolved without further escalation of national measures. The derived forecasting model attained accurate predictions of the daily infected cases, attaining a high goodness-of-fit, utilising uncensored government-official infection-rate and swabbing-rate data within the first COVID-19 wave in Malta.
Collapse
Affiliation(s)
- Mitchell G. Borg
- Department of Mechanical Engineering, Faculty of Engineering, University of Malta, MSD 2080 Msida, Malta
- Department of Naval Architecture, Ocean, and Marine Engineering, University of Strathclyde, Glasgow G4 0LZ, UK
| | - Michael A. Borg
- Infection Control Department, Mater Dei Hospital, MSD 2090 Msida, Malta
- Department of Microbiology, Faculty of Medicine and Surgery, University of Malta, MSD 2080 Msida, Malta
- Correspondence:
| |
Collapse
|
6
|
Adhikari K, Gautam R, Pokharel A, Dhimal M, Uprety KN, Vaidya NK. Insight into Delta variant dominated second wave of COVID-19 in Nepal. Epidemics 2022; 41:100642. [PMID: 36223673 PMCID: PMC9535929 DOI: 10.1016/j.epidem.2022.100642] [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: 12/10/2021] [Revised: 09/06/2022] [Accepted: 10/05/2022] [Indexed: 02/05/2023] Open
Abstract
OBJECTIVE To study the spreading nature of Delta variant (B.1.617.2) dominated COVID-19 in Nepal to help the policymakers assess and manage health care facilities and vaccination programs. METHODS Deterministic mathematical models in the form of systems of ordinary differential equations were developed to describe the COVID-19 transmission in the high- and the low-risk regions of Nepal. The models were validated using the multiple data sets containing daily new cases in the whole country, the high-risk region, the low-risk region, and cases needing medical care, ICU, and ventilator. RESULTS We found the reproduction number of Rt=4.2 at the beginning of the second wave, larger than the first wave (∼1.8 estimated previously), indicating that the transmissibility of Delta variant is higher than the wild-type circulated during the first wave. Model predicts that ∼5% of the COVID-19 cases were reported in Nepal, estimating the seroprevalence of ∼63.9% as of July 2021, consistent with the survey conducted by the Government of Nepal. The seroprevalence was expected to reach 94.46% by April 2022, among which ∼46% would have both infection and vaccination. The expected cases from September 2021 to April 2022 is 111,300, among which 11,890 people might need medical care, 3590 need ICU, and 953 need ventilators. The COVID-19 cases and medical care needs could be significantly reduced with proper implementation of vaccination and social distancing. CONCLUSIONS The data-driven mathematical models are useful to assess control programs in resource-limited countries. The appropriate combination of vaccination and social distancing are necessary to keep the pandemic under-control and manage the medical care facilities in Nepal.
Collapse
Affiliation(s)
| | - Ramesh Gautam
- Ratna Rajya Laxmi Campus, Tribhuvan University, Kathmandu, Nepal
| | - Anjana Pokharel
- Padma Kanya Multiple Campus, Tribhuvan University, Kathmandu, Nepal
| | - Meghnath Dhimal
- Nepal Health Research Council, Kathmandu, Nepal,Global Institute for Interdisciplinary Studies, Lalitpur, Nepal
| | - Kedar Nath Uprety
- Central Department of Mathematics, Tribhuvan University, Kathmandu, Nepal
| | - Naveen K. Vaidya
- Department of Mathematics and Statistics, San Diego State University, San Diego, CA, USA,Computational Science Research Center, San Diego State University, San Diego, CA, USA,Viral Information Institute, San Diego State University, San Diego, CA, USA,Correspondence to: Department of Mathematics and Statistics, San Diego State University, 5500 Campanile Drive, San Diego, CA 92182 USA
| |
Collapse
|
7
|
Zhang XS, Xiong H, Chen Z, Liu W. Importation, Local Transmission, and Model Selection in Estimating the Transmissibility of COVID-19: The Outbreak in Shaanxi Province of China as a Case Study. Trop Med Infect Dis 2022; 7:tropicalmed7090227. [PMID: 36136638 PMCID: PMC9502723 DOI: 10.3390/tropicalmed7090227] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2022] [Revised: 08/30/2022] [Accepted: 08/31/2022] [Indexed: 01/27/2023] Open
Abstract
Background: Since the emergence of the COVID-19 pandemic, many models have been applied to understand its epidemiological characteristics. However, the ways in which outbreak data were used in some models are problematic, for example, importation was mixed up with local transmission. Methods: In this study, five models were proposed for the early Shaanxi outbreak in China. We demonstrated how to select a reasonable model and correctly use the outbreak data. Bayesian inference was used to obtain parameter estimates. Results: Model comparison showed that the renewal equation model generates the best model fitting and the Susceptible-Exposed-Diseased-Asymptomatic-Recovered (SEDAR) model is the worst; the performance of the SEEDAR model, which divides the exposure into two stages and includes the pre-symptomatic transmission, and SEEDDAAR model, which further divides infectious classes into two equally, lies in between. The Richards growth model is invalidated by its continuously increasing prediction. By separating continuous importation from local transmission, the basic reproduction number of COVID-19 in Shaanxi province ranges from 0.45 to 0.61, well below the unit, implying that timely interventions greatly limited contact between people and effectively contained the spread of COVID-19 in Shaanxi. Conclusions: The renewal equation model provides the best modelling; mixing continuous importation with local transmission significantly increases the estimate of transmissibility.
Collapse
Affiliation(s)
- Xu-Sheng Zhang
- Statistics, Modelling and Economics, Data, Analytics & Surveillance, UK Health Security Agency, London NW9 5EQ, UK
- Correspondence:
| | - Huan Xiong
- School of Public Health, Kunming Medical University, Kunming 650500, China
| | - Zhengji Chen
- School of Public Health, Kunming Medical University, Kunming 650500, China
| | - Wei Liu
- School of Public Health, Kunming Medical University, Kunming 650500, China
| |
Collapse
|
8
|
Bhattarai S, Adhikari A, Rayamajhee B, Dhungana J, Singh M, Koirala S, Shakya DR. Participatory Approach to Develop Evidence-Based Clinical Ethics Guidelines for the Care of COVID-19 Patients: A Mixed Method Study From Nepal. Front Public Health 2022; 10:873881. [PMID: 35832276 PMCID: PMC9272001 DOI: 10.3389/fpubh.2022.873881] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2022] [Accepted: 05/24/2022] [Indexed: 11/13/2022] Open
Abstract
During health emergencies such as the COVID-19 pandemic, healthcare workers face numerous ethical challenges while catering to the needs of patients in healthcare settings. Although the data recapitulating high-income countries ethics frameworks are available, the challenges faced by clinicians in resource-limited settings of low- and middle-income countries are not discussed widely due to a lack of baseline data or evidence. The Nepali healthcare system, which is chronically understaffed and underequipped, was severely affected by the COVID-19 pandemic in its capacity to manage health services and resources for needy patients, leading to ethical dilemmas and challenges during clinical practice. This study aimed to develop a standard guideline that would address syndemic ethical dilemmas during clinical care of COVID-19 patients who are unable to afford standard-of-care. A mixed method study was conducted between February and June of 2021 in 12 government designated COVID-19 treatment hospitals in central Nepal. The draft guideline was discussed among the key stakeholders in the pandemic response in Nepal. The major ethical dilemmas confronted by the study participants (50 healthcare professionals providing patient care at COVID-19 treatment hospitals) could be grouped into five major pillars of ethical clinical practice: rational allocation of medical resources, updated treatment protocols that guide clinical decisions, standard-of-care regardless of patient's economic status, effective communication among stakeholders for prompt patient care, and external factors such as political and bureaucratic interference affecting ethical practice. This living clinical ethics guideline, which has been developed based on the local evidence and case stories of frontline responders, is expected to inform the policymakers as well as the decision-makers positioned at the concerned government units. These ethics guidelines could be endorsed with revisions by the concerned regulatory authorities for the use during consequent waves of COVID-19 and other epidemics that may occur in the future. Other countries affected by the pandemic could conduct similar studies to explore ethical practices in the local clinical and public health context.
Collapse
Affiliation(s)
- Suraj Bhattarai
- Department of Global Health, Global Institute for Interdisciplinary Studies (GIIS), Kathmandu, Nepal
| | - Anurag Adhikari
- Department of Infection and Immunology, Kathmandu Research Institute for Biological Sciences (KRIBS), Lalitpur, Nepal
| | - Binod Rayamajhee
- Department of Infection and Immunology, Kathmandu Research Institute for Biological Sciences (KRIBS), Lalitpur, Nepal
- School of Optometry and Vision Science, Faculty of Medicine and Health, UNSW, Sydney, NSW, Australia
| | - Jaya Dhungana
- Department of Global Health, Global Institute for Interdisciplinary Studies (GIIS), Kathmandu, Nepal
| | - Minu Singh
- Department of Infection and Immunology, Kathmandu Research Institute for Biological Sciences (KRIBS), Lalitpur, Nepal
| | - Sarun Koirala
- Department of Anatomy, B.P. Koirala Institute of Health Sciences (BPKIHS), Dharan, Nepal
- Nepali Unit of International Chair in Bioethics, Dharan, Nepal
| | - Dhana Ratna Shakya
- Nepali Unit of International Chair in Bioethics, Dharan, Nepal
- Department of Psychiatry, B.P. Koirala Institute of Health Sciences (BPKIHS), Dharan, Nepal
| |
Collapse
|