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Haq Z, Nazir J, Manzoor T, Saleem A, Hamadani H, Khan AA, Saleem Bhat S, Jha P, Ahmad SM. Zoonotic spillover and viral mutations from low and middle-income countries: improving prevention strategies and bridging policy gaps. PeerJ 2024; 12:e17394. [PMID: 38827296 PMCID: PMC11144393 DOI: 10.7717/peerj.17394] [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: 10/02/2023] [Accepted: 04/25/2024] [Indexed: 06/04/2024] Open
Abstract
The increasing frequency of zoonotic spillover events and viral mutations in low and middle-income countries presents a critical global health challenge. Contributing factors encompass cultural practices like bushmeat consumption, wildlife trade for traditional medicine, habitat disruption, and the encroachment of impoverished settlements onto natural habitats. The existing "vaccine gap" in many developing countries exacerbates the situation by allowing unchecked viral replication and the emergence of novel mutant viruses. Despite global health policies addressing the root causes of zoonotic disease emergence, there is a significant absence of concrete prevention-oriented initiatives, posing a potential risk to vulnerable populations. This article is targeted at policymakers, public health professionals, researchers, and global health stakeholders, particularly those engaged in zoonotic disease prevention and control in low and middle-income countries. The article underscores the importance of assessing potential zoonotic diseases at the animal-human interface and comprehending historical factors contributing to spillover events. To bridge policy gaps, comprehensive strategies are proposed that include education, collaborations, specialized task forces, environmental sampling, and the establishment of integrated diagnostic laboratories. These strategies advocate simplicity and unity, breaking down barriers, and placing humanity at the forefront of addressing global health challenges. Such a strategic and mental shift is crucial for constructing a more resilient and equitable world in the face of emerging zoonotic threats.
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Affiliation(s)
- Zulfqarul Haq
- ICMR project, Division of Livestock Production and Management, Sher-e-Kashmir University of Agricultural Sciences and Technology of Kashmir, India, Srinagar, Jammu and Kashmir, India
| | - Junaid Nazir
- Department of Clinical Biochemistry, Lovely Professional University, Phagwara, Punjab, India
- Division of Animal Biotechnology, Faculty of veterinary Sciences, Sher-e-Kashmir University of Agricultural Sciences and Technology of Kashmir, India, Srinagar, Jammu and Kashmir, India
| | - Tasaduq Manzoor
- Division of Animal Biotechnology, Faculty of veterinary Sciences, Sher-e-Kashmir University of Agricultural Sciences and Technology of Kashmir, India, Srinagar, Jammu and Kashmir, India
| | - Afnan Saleem
- Division of Animal Biotechnology, Faculty of veterinary Sciences, Sher-e-Kashmir University of Agricultural Sciences and Technology of Kashmir, India, Srinagar, Jammu and Kashmir, India
| | - H. Hamadani
- ICMR project, Division of Livestock Production and Management, Sher-e-Kashmir University of Agricultural Sciences and Technology of Kashmir, India, Srinagar, Jammu and Kashmir, India
| | - Azmat Alam Khan
- ICMR project, Division of Livestock Production and Management, Sher-e-Kashmir University of Agricultural Sciences and Technology of Kashmir, India, Srinagar, Jammu and Kashmir, India
| | - Sahar Saleem Bhat
- Division of Animal Biotechnology, Faculty of veterinary Sciences, Sher-e-Kashmir University of Agricultural Sciences and Technology of Kashmir, India, Srinagar, Jammu and Kashmir, India
| | - Priyanka Jha
- Department of Clinical Biochemistry, Lovely Professional University, Phagwara, Punjab, India
| | - Syed Mudasir Ahmad
- Division of Animal Biotechnology, Faculty of veterinary Sciences, Sher-e-Kashmir University of Agricultural Sciences and Technology of Kashmir, India, Srinagar, Jammu and Kashmir, India
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Yadav SK, Khan SA, Tiwari M, Kumar A, Kumar V, Akhter Y. Taking cues from machine learning, compartmental and time series models for SARS-CoV-2 omicron infection in Indian provinces. Spat Spatiotemporal Epidemiol 2024; 48:100634. [PMID: 38355258 DOI: 10.1016/j.sste.2024.100634] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/15/2023] [Revised: 11/15/2023] [Accepted: 01/15/2024] [Indexed: 02/16/2024]
Abstract
SARS-CoV-2, the virus responsible for COVID-19, posed a significant threat to the world. We analyzed COVID-19 dissemination data in the top ten Indian provinces by infection incidences using the Susceptible-Infectious-Removed (SIR) model, an Autoregressive Integrated Moving Average (ARIMA) time series model, a machine learning model based on the Random Forest, and distribution fitting. Outbreaks are expected to continue if the Basic Reproduction Number (R0) > 1, and infection waves are anticipated to end if the R0 < 1, as determined by the SIR model. Different parametric probability distributions are also fitted. Data collected from December 12, 2021, to March 31, 2022, encompassing data from both before and during the implementation of strict control measures. Based on the estimates of the model parameters, health agencies and government policymakers can develop strategies to combat the spread of the disease in the future, and the most effective technique can be recommended for real-world application for other outbreaks of COVID-19. The best method out of these could be also implemented further on the epidemiological data of other similar infectious agents.
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Affiliation(s)
- Subhash Kumar Yadav
- Department of Statistics, School of Physical and Decision Science, Babasaheb Bhimrao Ambedkar University, Lucknow-226025, India
| | - Saif Ali Khan
- Department of Statistics, School of Physical and Decision Science, Babasaheb Bhimrao Ambedkar University, Lucknow-226025, India
| | - Mayank Tiwari
- Department of Statistics, School of Physical and Decision Science, Babasaheb Bhimrao Ambedkar University, Lucknow-226025, India
| | - Arun Kumar
- Department of Statistics, School of Physical and Decision Science, Babasaheb Bhimrao Ambedkar University, Lucknow-226025, India
| | - Vinit Kumar
- Department of Library & Information Science, School of Information Science & Technology, Babasaheb Bhimrao Ambedkar University, Lucknow 226025, India
| | - Yusuf Akhter
- Department of Biotechnology, School of Life Sciences, Babasaheb Bhimrao Ambedkar University, Lucknow-226025, India.
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Ogwel B, Mzazi V, Nyawanda BO, Otieno G, Omore R. Predictive modeling for infectious diarrheal disease in pediatric populations: A systematic review. Learn Health Syst 2024; 8:e10382. [PMID: 38249852 PMCID: PMC10797570 DOI: 10.1002/lrh2.10382] [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: 01/07/2023] [Revised: 07/09/2023] [Accepted: 07/17/2023] [Indexed: 01/23/2024] Open
Abstract
Introduction Diarrhea is still a significant global public health problem. There are currently no systematic evaluation of the modeling areas and approaches to predict diarrheal illness outcomes. This paper reviews existing research efforts in predictive modeling of infectious diarrheal illness in pediatric populations. Methods We conducted a systematic review via a PubMed search for the period 1990-2021. A comprehensive search query was developed through an iterative process and literature on predictive modeling of diarrhea was retrieved. The following filters were applied to the search results: human subjects, English language, and children (birth to 18 years). We carried out a narrative synthesis of the included publications. Results Our literature search returned 2671 articles. After manual evaluation, 38 of these articles were included in this review. The most common research topic among the studies were disease forecasts 14 (36.8%), vaccine-related predictions 9 (23.7%), and disease/pathogen detection 5 (13.2%). Majority of these studies were published between 2011 and 2020, 28 (73.7%). The most common technique used in the modeling was machine learning 12 (31.6%) with various algorithms used for the prediction tasks. With change in the landscape of diarrheal etiology after rotavirus vaccine introduction, many open areas (disease forecasts, disease detection, and strain dynamics) remain for pathogen-specific predictive models among etiological agents that have emerged as important. Additionally, the outcomes of diarrheal illness remain under researched. We also observed lack of consistency in the reporting of results of prediction models despite the available guidelines highlighting the need for common data standards and adherence to guidelines on reporting of predictive models for biomedical research. Conclusions Our review identified knowledge gaps and opportunities in predictive modeling for diarrheal illness, and limitations in existing attempts whilst advancing some precursory thoughts on how to address them, aiming to invigorate future research efforts in this sphere.
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Affiliation(s)
- Billy Ogwel
- Kenya Medical Research Institute, Center for Global Health Research (KEMRI‐CGHR)KisumuKenya
- Department of Information SystemsUniversity of South AfricaPretoriaSouth Africa
| | - Vincent Mzazi
- Department of Information SystemsUniversity of South AfricaPretoriaSouth Africa
| | - Bryan O. Nyawanda
- Kenya Medical Research Institute, Center for Global Health Research (KEMRI‐CGHR)KisumuKenya
| | - Gabriel Otieno
- Department of ComputingUnited States International UniversityNairobiKenya
| | - Richard Omore
- Kenya Medical Research Institute, Center for Global Health Research (KEMRI‐CGHR)KisumuKenya
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Hulme PE, Beggs JR, Binny RN, Bray JP, Cogger N, Dhami MK, Finlay-Smits SC, French NP, Grant A, Hewitt CL, Jones EE, Lester PJ, Lockhart PJ. Emerging advances in biosecurity to underpin human, animal, plant, and ecosystem health. iScience 2023; 26:107462. [PMID: 37636074 PMCID: PMC10450416 DOI: 10.1016/j.isci.2023.107462] [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] [Indexed: 08/29/2023] Open
Abstract
One Biosecurity is an interdisciplinary approach to policy and research that builds on the interconnections between human, animal, plant, and ecosystem health to effectively prevent and mitigate the impacts of invasive alien species. To support this approach requires that key cross-sectoral research innovations be identified and prioritized. Following an interdisciplinary horizon scan for emerging research that underpins One Biosecurity, four major interlinked advances were identified: implementation of new surveillance technologies adopting state-of-the-art sensors connected to the Internet of Things, deployable handheld molecular and genomic tracing tools, the incorporation of wellbeing and diverse human values into biosecurity decision-making, and sophisticated socio-environmental models and data capture. The relevance and applicability of these innovations to address threats from pathogens, pests, and weeds in both terrestrial and aquatic ecosystems emphasize the opportunity to build critical mass around interdisciplinary teams at a global scale that can rapidly advance science solutions targeting biosecurity threats.
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Affiliation(s)
- Philip E. Hulme
- The Centre for One Biosecurity Research, Analysis and Synthesis, Lincoln University, PO Box 85084, Lincoln, Christchurch 7648, New Zealand
- Department of Pest Management and Conservation, Lincoln University, PO Box 85084, Lincoln, Christchurch 7648, New Zealand
| | - Jacqueline R. Beggs
- Centre for Biodiversity and Biosecurity, School of Biological Sciences, University of Auckland, Private Bag 92019, Auckland 1142, New Zealand
| | - Rachelle N. Binny
- Manaaki Whenua - Landcare Research, PO Box 69040, Lincoln, New Zealand
| | - Jonathan P. Bray
- The Centre for One Biosecurity Research, Analysis and Synthesis, Lincoln University, PO Box 85084, Lincoln, Christchurch 7648, New Zealand
- Department of Pest Management and Conservation, Lincoln University, PO Box 85084, Lincoln, Christchurch 7648, New Zealand
| | - Naomi Cogger
- Tāwharau Ora, School of Veterinary Science, Massey University, Palmerston North 4472, New Zealand
| | - Manpreet K. Dhami
- Manaaki Whenua - Landcare Research, PO Box 69040, Lincoln, New Zealand
| | | | - Nigel P. French
- Tāwharau Ora, School of Veterinary Science, Massey University, Palmerston North 4472, New Zealand
| | - Andrea Grant
- Scion, 10 Kyle Street, Riccarton, Christchurch 8011, New Zealand
| | - Chad L. Hewitt
- The Centre for One Biosecurity Research, Analysis and Synthesis, Lincoln University, PO Box 85084, Lincoln, Christchurch 7648, New Zealand
| | - Eirian E. Jones
- The Centre for One Biosecurity Research, Analysis and Synthesis, Lincoln University, PO Box 85084, Lincoln, Christchurch 7648, New Zealand
- Department of Pest Management and Conservation, Lincoln University, PO Box 85084, Lincoln, Christchurch 7648, New Zealand
| | - Phil J. Lester
- School of Biological Sciences, Victoria University of Wellington, PO Box 600, Wellington, New Zealand
| | - Peter J. Lockhart
- School of Natural Sciences, Massey University, Palmerston North 4472, New Zealand
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Sciannameo V, Azzolina D, Lanera C, Acar AŞ, Corciulo MA, Comoretto RI, Berchialla P, Gregori D. Fitting Early Phases of the COVID-19 Outbreak: A Comparison of the Performances of Used Models. Healthcare (Basel) 2023; 11:2363. [PMID: 37628560 PMCID: PMC10454512 DOI: 10.3390/healthcare11162363] [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: 07/08/2023] [Revised: 08/06/2023] [Accepted: 08/17/2023] [Indexed: 08/27/2023] Open
Abstract
The COVID-19 outbreak involved a spread of prediction efforts, especially in the early pandemic phase. A better understanding of the epidemiological implications of the different models seems crucial for tailoring prevention policies. This study aims to explore the concordance and discrepancies in outbreak prediction produced by models implemented and used in the first wave of the epidemic. To evaluate the performance of the model, an analysis was carried out on Italian pandemic data from February 24, 2020. The epidemic models were fitted to data collected at 20, 30, 40, 50, 60, 70, 80, 90, and 98 days (the entire time series). At each time step, we made predictions until May 31, 2020. The Mean Absolute Error (MAE) and the Mean Absolute Percentage Error (MAPE) were calculated. The GAM model is the most suitable parameterization for predicting the number of new cases; exponential or Poisson models help predict the cumulative number of cases. When the goal is to predict the epidemic peak, GAM, ARIMA, or Bayesian models are preferable. However, the prediction of the pandemic peak could be made carefully during the early stages of the epidemic because the forecast is affected by high uncertainty and may very likely produce the wrong results.
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Affiliation(s)
- Veronica Sciannameo
- Unit of Biostatistics, Epidemiology and Public Health, Department of Cardiac, Thoracic, Vascular Sciences, and Public Health, University of Padova, 35131 Padova, Italy; (V.S.); (D.A.); (C.L.); (M.A.C.); (R.I.C.)
- Center of Biostatistics, Epidemiology and Public Health, Department of Clinical and Biological Sciences, University of Torino, 10124 Turin, Italy;
| | - Danila Azzolina
- Unit of Biostatistics, Epidemiology and Public Health, Department of Cardiac, Thoracic, Vascular Sciences, and Public Health, University of Padova, 35131 Padova, Italy; (V.S.); (D.A.); (C.L.); (M.A.C.); (R.I.C.)
- Department of Environmental and Preventive Sciences, University of Ferrara, 44121 Ferrara, Italy
| | - Corrado Lanera
- Unit of Biostatistics, Epidemiology and Public Health, Department of Cardiac, Thoracic, Vascular Sciences, and Public Health, University of Padova, 35131 Padova, Italy; (V.S.); (D.A.); (C.L.); (M.A.C.); (R.I.C.)
| | | | - Maria Assunta Corciulo
- Unit of Biostatistics, Epidemiology and Public Health, Department of Cardiac, Thoracic, Vascular Sciences, and Public Health, University of Padova, 35131 Padova, Italy; (V.S.); (D.A.); (C.L.); (M.A.C.); (R.I.C.)
| | - Rosanna Irene Comoretto
- Unit of Biostatistics, Epidemiology and Public Health, Department of Cardiac, Thoracic, Vascular Sciences, and Public Health, University of Padova, 35131 Padova, Italy; (V.S.); (D.A.); (C.L.); (M.A.C.); (R.I.C.)
- Department of Public Health and Pediatrics, University of Torino, 10124 Turin, Italy
| | - Paola Berchialla
- Center of Biostatistics, Epidemiology and Public Health, Department of Clinical and Biological Sciences, University of Torino, 10124 Turin, Italy;
| | - Dario Gregori
- Unit of Biostatistics, Epidemiology and Public Health, Department of Cardiac, Thoracic, Vascular Sciences, and Public Health, University of Padova, 35131 Padova, Italy; (V.S.); (D.A.); (C.L.); (M.A.C.); (R.I.C.)
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Aragão DP, Junior AGDS, Mondini A, Distante C, Gonçalves LMG. COVID-19 Patterns in Araraquara, Brazil: A Multimodal Analysis. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2023; 20:4740. [PMID: 36981646 PMCID: PMC10048455 DOI: 10.3390/ijerph20064740] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/26/2023] [Revised: 03/03/2023] [Accepted: 03/04/2023] [Indexed: 06/18/2023]
Abstract
The epidemiology of COVID-19 presented major shifts during the pandemic period. Factors such as the most common symptoms and severity of infection, the circulation of different variants, the preparedness of health services, and control efforts based on pharmaceutical and non-pharmaceutical interventions played important roles in the disease incidence. The constant evolution and changes require the continuous mapping and assessing of epidemiological features based on time-series forecasting. Nonetheless, it is necessary to identify the events, patterns, and actions that were potential factors that affected daily COVID-19 cases. In this work, we analyzed several databases, including information on social mobility, epidemiological reports, and mass population testing, to identify patterns of reported cases and events that may indicate changes in COVID-19 behavior in the city of Araraquara, Brazil. In our analysis, we used a mathematical approach with the fast Fourier transform (FFT) to map possible events and machine learning model approaches such as Seasonal Auto-regressive Integrated Moving Average (ARIMA) and neural networks (NNs) for data interpretation and temporal prospecting. Our results showed a root-mean-square error (RMSE) of about 5 (more precisely, a 4.55 error over 71 cases for 20 March 2021 and a 5.57 error over 106 cases for 3 June 2021). These results demonstrated that FFT is a useful tool for supporting the development of the best prevention and control measures for COVID-19.
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Affiliation(s)
- Dunfrey Pires Aragão
- Pós-Graduação em Engenharia Elétrica e de Computação, Universidade Federal do Rio Grande do Norte, Av. Salgado Filho, 3000, Lagoa Nova, Natal 59078-970, Brazil
- Institute of Applied Sciences and Intelligent Systems-CNR, Via Monteroni sn, 73100 Lecce, Italy
| | | | - Adriano Mondini
- Faculdade de Ciências Farmacêuticas, Universidade Estadual Paulista “Júlio de Mesquita Filho”, Rodovia Araraquara-Jaú, Km 1, Campus Ville, Araraquara 14800-903, Brazil
| | - Cosimo Distante
- Institute of Applied Sciences and Intelligent Systems-CNR, Via Monteroni sn, 73100 Lecce, Italy
| | - Luiz Marcos Garcia Gonçalves
- Pós-Graduação em Engenharia Elétrica e de Computação, Universidade Federal do Rio Grande do Norte, Av. Salgado Filho, 3000, Lagoa Nova, Natal 59078-970, Brazil
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McClymont H, Si X, Hu W. Using weather factors and google data to predict COVID-19 transmission in Melbourne, Australia: A time-series predictive model. Heliyon 2023; 9:e13782. [PMID: 36845036 PMCID: PMC9941072 DOI: 10.1016/j.heliyon.2023.e13782] [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: 05/05/2022] [Revised: 02/09/2023] [Accepted: 02/10/2023] [Indexed: 02/23/2023] Open
Abstract
Background Forecast models have been essential in understanding COVID-19 transmission and guiding public health responses throughout the pandemic. This study aims to assess the effect of weather variability and Google data on COVID-19 transmission and develop multivariable time series AutoRegressive Integrated Moving Average (ARIMA) models for improving traditional predictive modelling for informing public health policy. Methods COVID-19 case notifications, meteorological factors and Google data were collected over the B.1.617.2 (Delta) outbreak in Melbourne, Australia from August to November 2021. Timeseries cross-correlation (TSCC) was used to evaluate the temporal correlation between weather factors, Google search trends, Google Mobility data and COVID-19 transmission. Multivariable time series ARIMA models were fitted to forecast COVID-19 incidence and Effective Reproductive Number (R eff ) in the Greater Melbourne region. Five models were fitted to compare and validate predictive models using moving three-day ahead forecasts to test the predictive accuracy for both COVID-19 incidence and R eff over the Melbourne Delta outbreak. Results Case-only ARIMA model resulted in an R squared (R2) value of 0.942, Root Mean Square Error (RMSE) of 141.59, and Mean Absolute Percentage Error (MAPE) of 23.19. The model including transit station mobility (TSM) and maximum temperature (Tmax) had greater predictive accuracy with R2 0.948, RMSE 137.57, and MAPE 21.26. Conclusion Multivariable ARIMA modelling for COVID-19 cases and R eff was useful for predicting epidemic growth, with higher predictive accuracy for models including TSM and Tmax. These results suggest that TSM and Tmax would be useful for further exploration for developing weather-informed early warning models for future COVID-19 outbreaks with potential application for the inclusion of weather and Google data with disease surveillance in developing effective early warning systems for informing public health policy and epidemic response.
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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.
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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:
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Aekphachaisawat N, Sawanyawisuth K, Khamsai S, Boonsawat W, Tiamkao S, Limpawattana P, Maleewong W, Ngamjarus C. A national surveillance of eosinophilic meningitis in Thailand. Parasite Epidemiol Control 2022; 19:e00272. [PMID: 36133000 PMCID: PMC9483718 DOI: 10.1016/j.parepi.2022.e00272] [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: 10/13/2021] [Revised: 07/27/2022] [Accepted: 09/04/2022] [Indexed: 11/26/2022] Open
Abstract
Introduction Eosinophilic meningitis (EOM) is an emerging infectious disease worldwide. The most common cause of EOM is infection with Angiostrongylus cantonensis One possible method of monitoring and control of this infection is surveillance and prediction. There are limited data on national surveillance and predictive models on EOM. This study aimed to develop an online surveillance with a predictive model for EOM by using the national database. Methods We retrospectively retrieved reported cases of EOM from all provinces in Thailand and quantified them by month and year. Data were retrieved from Ministry of Public Health database. We developed a website application to explore the EOM cases in Thailand including regions and provinces using box plots. The website also provided the Autoregressive Integrated Moving Average (ARIMA) models and Seasonal ARIMA (SARIMA) models for predicting the disease cases from nation, region, and province levels. The suitable models were considered by minimum Akaike Information Criterion (AIC). The appropriate SARIMA model was used to predict the number of EOM cases. Results From 2003 to 2021, 3330 EOM cases were diagnosed and registered in the national database, with a peak in 2003 (median of 22 cases). We determined SARIMA(1,1,2)(2,0,0)[12] to be the most appropriate model, as it yielded the fitted values that were closest to the actual data. A predictive surveillance website was published on http://202.28.75.8/sample-apps/NationalEOM/. Conclusions We determined that web application can be used for monitoring and exploring the trend of EOM patients in Thailand. The predictive values matched the actual monthly numbers of EOM cases indicating a good fit of the predictive model.
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Affiliation(s)
| | | | - Sittichai Khamsai
- Department of Medicine, Faculty of Medicine, Khon Kaen University, Khon Kaen, Thailand
| | - Watchara Boonsawat
- Department of Medicine, Faculty of Medicine, Khon Kaen University, Khon Kaen, Thailand
| | - Somsak Tiamkao
- Department of Medicine, Faculty of Medicine, Khon Kaen University, Khon Kaen, Thailand
| | - Panita Limpawattana
- Department of Medicine, Faculty of Medicine, Khon Kaen University, Khon Kaen, Thailand
| | - Wanchai Maleewong
- Department of Parasitology, Faculty of Medicine, Khon Kaen University, Khon Kaen, Thailand
| | - Chetta Ngamjarus
- Department of Epidemiology and Biostatistics, Faculty of Public Health, Khon Kaen University, Khon Kaen, Thailand
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Branda F, Abenavoli L, Pierini M, Mazzoli S. Predicting the Spread of SARS-CoV-2 in Italian Regions: The Calabria Case Study, February 2020-March 2022. Diseases 2022; 10:38. [PMID: 35892732 PMCID: PMC9326619 DOI: 10.3390/diseases10030038] [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: 05/30/2022] [Revised: 06/23/2022] [Accepted: 06/28/2022] [Indexed: 12/27/2022] Open
Abstract
Despite the stunning speed with which highly effective and safe vaccines have been developed, the emergence of new variants of SARS-CoV-2 causes high rates of (re)infection, a major impact on health care services, and a slowdown to the socio-economic system. For COVID-19, accurate and timely forecasts are therefore essential to provide the opportunity to rapidly identify risk areas affected by the pandemic, reallocate the use of health resources, design countermeasures, and increase public awareness. This paper presents the design and implementation of an approach based on autoregressive models to reliably forecast the spread of COVID-19 in Italian regions. Starting from the database of the Italian Civil Protection Department (DPC), the experimental evaluation was performed on real-world data collected from February 2020 to March 2022, focusing on Calabria, a region of Southern Italy. This evaluation shows that the proposed approach achieves a good predictive power for out-of-sample predictions within one week (R-squared > 0.9 at 1 day, R-squared > 0.7 at 7 days), although it decreases with increasing forecasted days (R-squared > 0.5 at 14 days).
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Affiliation(s)
- Francesco Branda
- Department of Computer Science, Modeling, Electronics and Systems Engineering (DIMES), University of Calabria, 87036 Rende, Italy;
| | - Ludovico Abenavoli
- Department of Health Sciences, University Magna Graecia, 88100 Catanzaro, Italy
| | - Massimo Pierini
- Guglielmo Marconi University, 00193 Rome, Italy;
- SITO WEB del Gruppo Epidemiologico, EpiData.it, 24121 Bergamo, Italy;
| | - Sandra Mazzoli
- SITO WEB del Gruppo Epidemiologico, EpiData.it, 24121 Bergamo, Italy;
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Gulumbe BH, Bazata AY, Bagwai MA. Campylobacter Species, Microbiological Source Tracking and Risk Assessment of Bacterial pathogens. BORNEO JOURNAL OF PHARMACY 2022. [DOI: 10.33084/bjop.v5i2.3363] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022] Open
Abstract
Campylobacter species continue to remain critical pathogens of public health interest. They are responsible for approximately 500 million cases of gastroenteritis per year worldwide. Infection occurs through the consumption of contaminated food and water. Microbial risk assessment and source tracking are crucial epidemiological strategies to monitor the outbreak of campylobacteriosis effectively. Various methods have been proposed for microbial source tracking and risk assessment, most of which rely on conventional microbiological techniques such as detecting fecal indicator organisms and other novel microbial source tracking methods, including library-dependent microbial source tracking and library-independent source tracking approaches. However, both the traditional and novel methods have their setbacks. For example, while the conventional techniques are associated with a poor correlation between indicator organism and pathogen presence, on the other hand, it is impractical to interpret qPCR-generated markers to establish the exact human health risks even though it can give information regarding the potential source and relative human risk. Therefore, this article provides up-to-date information on campylobacteriosis, various approaches for source attribution, and risk assessment of bacterial pathogens, including next-generation sequencing approaches such as shotgun metagenomics, which effectively answer the questions of potential pathogens are there and in what quantities.
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Sundar S, Schwab P, Tan JZH, Romero-Brufau S, Celi LA, Wangmo D, Penna ND. Forecasting the COVID-19 Pandemic: Lessons learned and future directions. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2021:2021.11.06.21266007. [PMID: 34806093 PMCID: PMC8603143 DOI: 10.1101/2021.11.06.21266007] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
I.The Coronavirus Disease 2019 (COVID-19) has demonstrated that accurate forecasts of infection and mortality rates are essential for informing healthcare resource allocation, designing countermeasures, implementing public health policies, and increasing public awareness. However, there exist a multitude of modeling methodologies, and their relative performances in accurately forecasting pandemic dynamics are not currently comprehensively understood. In this paper, we introduce the non-mechanistic MIT-LCP forecasting model, and assess and compare its performance to various mechanistic and non-mechanistic models that have been proposed for forecasting COVID-19 dynamics. We performed a comprehensive experimental evaluation which covered the time period of November 2020 to April 2021, in order to determine the relative performances of MIT-LCP and seven other forecasting models from the United States' Centers for Disease Control and Prevention (CDC) Forecast Hub. Our results show that there exist forecasting scenarios well-suited to both mechanistic and non-mechanistic models, with mechanistic models being particularly performant for forecasts that are further in the future when recent data may not be as informative, and non-mechanistic models being more effective with shorter prediction horizons when recent representative data is available. Improving our understanding of which forecasting approaches are more reliable, and in which forecasting scenarios, can assist effective pandemic preparation and management.
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