Improving epidemic size prediction through stable reconstruction of disease parameters by reduced iteratively regularized Gauss–Newton algorithm

2017 ◽  
Vol 25 (5) ◽  
pp. 653-667 ◽  
Author(s):  
Alexandra Smirnova ◽  
Gerardo Chowell-Puente ◽  
Linda deCamp ◽  
Seyed Moghadas ◽  
Michael Jameson Sheppard
Keyword(s):  
Phenomenological Models ◽  
Public Health Intervention ◽  
Epidemiological Data ◽  
Risk Groups ◽  
Stable Algorithm ◽  
Incidence Data ◽  
Epidemic Size ◽  
Optimal Resource ◽  
Non Linear Operator ◽  
Stable Reconstruction

AbstractClassical compartmental epidemic models of infectious diseases track the dynamic transition of individuals between different epidemiological states or risk groups. Reliable quantification of various transmission pathways in these models is paramount for optimal resource allocation and successful design of public health intervention programs. However, with limited epidemiological data available in the case of an emerging disease, simple phenomenological models based on a smaller number of parameters can play an important role in our quest to make forward projections of possible outbreak scenarios. In this paper, we employ the generalized Richards model for stable numerical estimation of the epidemic size (defined as the total number of infections throughout the epidemic) and its turning point using case incidence data of the early epidemic growth phase. The minimization is carried out by what we call the Reduced Iteratively Regularized Gauss–Newton (RIRGN) algorithm, a problem-oriented numerical scheme that takes full advantage of the specific structure of the non-linear operator at hand. The convergence analysis of the RIRGN method is suggested and numerical simulations are conducted with real case incidence data for the 2014–15 Ebola epidemic in West Africa. We show that the proposed RIRGN provides a stable algorithm for early estimation of turning points using simple phenomenological models with limited data.

scholarly journals A stochastic metapopulation state-space approach to modeling and estimating COVID-19 spread

2021 ◽  
Vol 18 (6) ◽  
pp. 7685-7710
Author(s):  
Yukun Tan ◽  
◽  
Durward Cator III ◽  
Martial Ndeffo-Mbah ◽  
Ulisses Braga-Neto ◽  
...  
Keyword(s):  
State Space ◽  
State Space Model ◽  
Synthetic Data ◽  
Public Health Intervention ◽  
Disease Outbreaks ◽  
Real Data ◽  
Epidemiological Data ◽  
Transmission Parameters ◽  
Intervention Measures ◽  
Spatio Temporal

<abstract><p>Mathematical models are widely recognized as an important tool for analyzing and understanding the dynamics of infectious disease outbreaks, predict their future trends, and evaluate public health intervention measures for disease control and elimination. We propose a novel stochastic metapopulation state-space model for COVID-19 transmission, which is based on a discrete-time spatio-temporal susceptible, exposed, infected, recovered, and deceased (SEIRD) model. The proposed framework allows the hidden SEIRD states and unknown transmission parameters to be estimated from noisy, incomplete time series of reported epidemiological data, by application of unscented Kalman filtering (UKF), maximum-likelihood adaptive filtering, and metaheuristic optimization. Experiments using both synthetic data and real data from the Fall 2020 COVID-19 wave in the state of Texas demonstrate the effectiveness of the proposed model.</p></abstract>

General aspects of the COVID-19 pandemic

2021 ◽  
Vol 21 (suppl 1) ◽  
pp. 29-45
Author(s):  
Alex Sandro Rolland Souza ◽  
Melania Maria Ramos Amorim ◽  
Adriana Suely de Oliveira Melo ◽  
Alexandre Magno Delgado ◽  
Anna Catharina Magliano Carneiro da Cunha Florêncio ◽  
...  
Keyword(s):  
Literature Review ◽  
Mortality Risk ◽  
Strong Evidence ◽  
Epidemiological Data ◽  
Risk Groups ◽  
High Rate ◽  
Narrative Literature ◽  
Transmission Prevention ◽  
Narrative Literature Review ◽  
General Aspects

Abstract Objectives: to review the available literature on the general aspects of SARS-CoV-2 infec-tion. Methods: this is a narrative literature review carried out from March to September 2020. Results: COVID-19 caused by the new coronavirus or SARS-CoV-2, grows with devas-tating effects worldwide. The literature describes epidemiological data and mortality risk groups of the disease, which presents a high rate of transmission. Prevention is the most effective way to fight the disease, persisting the absence of strong evidence on the treatment. Vaccines are not yet available. Dexamethasone is effective in reducing mortality in severe forms. Conclusions: despite great efforts, as the number of confirmed cases increases, evidence on transmission, incidence, disease progression, lethality, effects and outcomes remain limited and without any high levels of evidence. Studies are still necessary for all aspects of the disease.

scholarly journals Contribution of transgender sex workers to the complexity of the HIV-1 epidemic in the metropolitan area of Milan

2020 ◽  
Vol 96 (6) ◽  
pp. 451-456 ◽  
Author(s):  
Alessia Lai ◽  
Annalisa Bergna ◽  
Francesco Roberto Simonetti ◽  
Marco Franzetti ◽  
Giorgio Bozzi ◽  
...  
Keyword(s):  
Sex Workers ◽  
Phylogenetic Analyses ◽  
Epidemiological Data ◽  
Risk Groups ◽  
Similar Proportion ◽  
South American ◽  
Subtype C ◽  
Transgender People ◽  
Clinical Records ◽  
Hiv 1

ObjectivesTransgender people are disproportionately affected by the HIV-1 epidemic. We evaluated the origin of HIV-1 variants carried by South American transgenders living in Milan by combining accurate phylogenetic methods and epidemiological data.MethodsWe collected 156 HIV-1 pol sequences obtained from transgender patients engaged in sex work (TSWs) followed between 1999 and 2015 at L. Sacco Hospital, Milan, Italy. Phylogenetic analyses were conducted by HIV-TRACE, MrBayes, MacClade and Beast programs. Reference sequences were retrieved from Los Alamos and local databases. Last negative testing or proxy data from clinical records of infected individuals were used to investigate the country of infection.ResultsAmong South American TSWs, the most represented HIV-1 subtypes were B (70.5%), F1 (12.8%) and C (4.4%). Gene flow migrations of B subtype indicated significant fluxes from TSWs to Italians (21.3%) belonging to all risk groups (26.4% to heterosexuals (HEs), 18.9% to men who have sex with men (MSM), 15.1% to injecting drug users). The largest proportion of bidirectional fluxes were observed between Italians and TSWs (24.6%). For F1 subtype, bidirectional viral fluxes involved TSWs and Italians (7.1% and 14.3%), and a similar proportion of fluxes linked TSWs and Italian HEs or MSM (both 15.8%). Significant fluxes were detected from Italians to TSWs for subtype C involving both MSM (30%) and HEs (40%). Country of HIV-1 acquisition was identified for 72 subjects; overall, the largest proportion of patients with B subtype (73.5%) acquired HIV-1 infection in South America.ConclusionsOur results indicated that South American transgenders largely contribute to the heterogeneity of HIV-1 variants in our country. The high number of clusters based on all subtypes indicated numerous transmission chains in which TSWs were constantly intermixed with HEs and MSM. Our results strongly advocate interventions to facilitate prevention, diagnosis and HIV-1 care continuum among transgender people.

scholarly journals O6B.6 Occupational radiation exposure and the risk of cancer and cardiovascular diseases among medical radiation workers

2019 ◽  
Vol 76 (Suppl 1) ◽  
pp. A54.3-A55
Author(s):  
Won Jin Lee ◽  
Eun Shil Cha ◽  
Seulki Ko
Keyword(s):  
Cardiovascular Diseases ◽  
Radiation Exposure ◽  
Risk Groups ◽  
Health Insurance Data ◽  
Potential Health ◽  
Radiologic Technologists ◽  
Incidence Data ◽  
Medical Radiation ◽  
Radiation Workers ◽  
Risk Of Cancer

BackgroundMedical radiation workers occupy the largest group of radiation workers and the numbers are rapidly increasing worldwide. This presentation purposes to report findings from the Korean medical radiation workers’ study.MethodsData on all diagnostic medical radiation workers enrolled at the national dose registry between 1996 and 2011 (n=94,396) were merged with the death and cancer incidence data with coverage through the end of 2015. We reconstructed historical radiation doses and estimated organ-specific doses for all workers. The cancer risks were calculated using standardized mortality ratios (SMRs), standardized incidence ratios (SIRs), excess relative risk (ERR), and lifetime attributable risk (LAR). For radiologic technologists (n=12,906), we conducted a survey and merged the data with the national health insurance data of 2006–2016 for investigation of non-cancer diseases.ResultsThe mean cumulative badge doses for all workers were 10.6 mSv (men) and 2.7 mSv (women). Medical radiation workers have more favorable mortality than in general population for all causes of death among men (SMR=0.45) and women (SMR=0.49). Based on 2192 of primary cancer cases, the SIR for all cancers significantly decreased in men (SIR=0.88) and increased in women (SIR=1.10). However, there were no significant ERRs of all cancer incidences in both men and women. LARs for all cancer combined ranged from 9 to 402 per 1 00 000 varied by sex and job title. Among radiologic technologists, the odds ratios for cardiovascular diseases showed a significant increasing trend with colon doses (trend p=0.024) after adjusting for potential risk factors.ConclusionsOur findings provide some evidence of occupation radiation exposure and its health effects among medical radiation workers. The risks were generally small but not acceptable at high risk groups. Continuous monitoring and further follow-up is warranted to optimize the work practices for the protection of potential health risks in medical radiation workers.

scholarly journals Lockdown Contained the Spread of 2019 Novel Coronavirus Disease in Huangshi City, China: Early Epidemiological Findings

2020 ◽  
Vol 71 (6) ◽  
pp. 1454-1460 ◽  
Author(s):  
Tuo Ji ◽  
Hai-Lian Chen ◽  
Jing Xu ◽  
Ling-Ning Wu ◽  
Jie-Jia Li ◽  
...  
Keyword(s):  
Temporal Dynamics ◽  
Public Health Intervention ◽  
Epidemiological Data ◽  
Field Investigation ◽  
Epidemic Curve ◽  
Adults And Children ◽  
Asymptomatic Infections ◽  
Novel Coronavirus ◽  
Nonpharmaceutical Interventions ◽  
Epidemiological Characteristics

Abstract Background To control the spread of 2019 novel coronavirus disease (COVID-19), China sealed Wuhan on 23 January 2020 and soon expanded lockdown to 12 other cities in Hubei province. We aimed to describe the epidemiological characteristics in one of the cities and highlight the effect of current implemented lockdown and nonpharmaceutical interventions. Methods We retrieved data of reported cases in Huangshi and Wuhan from publicly available disease databases. Local epidemiological data on suspected or confirmed cases in Huangshi were collected through field investigation. Epidemic curves were constructed with data on reported and observed cases. Results The accumulated confirmed COVID-19 cases and fatality in Huangshi were reported to be 1015 and 3.74%, respectively, compared with 50006 and 5.08% in Wuhan until 27 March 2020. Right after 24 January, the epidemic curve based on observed cases in Huangshi became flattened. And 1 February 2020 was identified as the “turning point” as the epidemic in Huangshi faded soon afterward. COVID-19 epidemic was characterized by mild cases in Huangshi, accounting for 82.66% of total cases. Moreover, 50 asymptomatic infections were identified in adults and children. In addition, we found confirmed cases in 19 familial clusters and 21 healthcare workers, supporting interhuman transmission. Conclusions Our study reported the temporal dynamics and characteristics of the COVID-19 epidemic in Huangshi city, China, across the unprecedented intervention. Such new epidemiological inference might provide further guidance on current lockdown measures in high-risk cities and, subsequently, help improve public health intervention strategies against the pandemic on the country and global levels.

scholarly journals Combating hepatitis B and C by 2030: achievements, gaps, and options for actions in China

2020 ◽  
Vol 5 (6) ◽  
pp. e002306
Author(s):  
Shu Chen ◽  
Wenhui Mao ◽  
Lei Guo ◽  
Jiahui Zhang ◽  
Shenglan Tang
Keyword(s):  
Information Systems ◽  
Hepatitis B ◽  
Viral Hepatitis ◽  
Large Scale ◽  
Epidemiological Data ◽  
Risk Groups ◽  
Chronic Viral Hepatitis ◽  
Nucleic Acid Amplification ◽  
Effective Control ◽  
Governance System

China has the highest number of hepatitis B and C cases globally. Despite remarkable achievements, China faces daunting challenges in achieving international targets for hepatitis elimination. As part of a large-scale project assessing China’s progress in achieving health-related Sustainable Development Goals using quantitative, qualitative data and mathematical modelling, this paper summarises the achievements, gaps and challenges, and proposes options for actions for hepatitis B and C control. China has made substantial progress in controlling chronic viral hepatitis. The four most successful strategies have been: (1) hepatitis B virus childhood immunisation; (2) prevention of mother-to-child transmission; (3) full coverage of nucleic acid amplification testing in blood stations and (4) effective financing strategies to support treatment. However, the total number of deaths due to hepatitis B and C is estimated to increase from 434 724 in 2017 to 527 829 in 2030 if there is no implementation of tailored interventions. Many health system barriers, including a fragmented governance system, insufficient funding, inadequate service coverage, unstandardised treatment and flawed information systems, have compromised the effective control of hepatitis B and C in China. We suggest five strategic priority actions to help eliminate hepatitis B and C in China: (1) restructure the viral hepatitis control governance system; (2) optimise health resource allocation and improve funding efficiency; (3) improve access to and the quality of the health benefits package, especially for high-risk groups; (4) strengthen information systems to obtain high-quality hepatitis epidemiological data; (5) increase investment in viral hepatitis research and development.

The epidemiology of multiple sclerosis in Latin America and the Caribbean: a systematic review

2012 ◽  
Vol 19 (7) ◽  
pp. 844-854 ◽  
Author(s):  
E Cristiano ◽  
JI Rojas ◽  
M Romano ◽  
N Frider ◽  
G Machnicki ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
Systematic Review ◽  
Latin America ◽  
Low Frequency ◽  
Epidemiological Data ◽  
Developed Countries ◽  
Epidemiological Studies ◽  
Incidence Data ◽  
Full Text Review ◽  
The Caribbean

The incidence and prevalence of multiple sclerosis (MS) varies geographically as shown through extensive epidemiological studies performed mainly in developed countries. Nonetheless, scant data is available in Latin America and the Caribbean (LAC). The objective of this review is to assess epidemiological data of MS in LAC. We conducted a systematic review of published articles and gray literature from January 1995 to May 2011. Twenty-two studies met the inclusion criteria after full-text review. Incidence data were found in only three studies and ranged from 0.3 to 1.9 annual cases per 100,000 person-years. Prevalence was reported in 10 studies and ranged from 0.83 to 21.5 cases per 100,000 inhabitants. The most prevalent subtype of MS was the relapsing–remitting form (48% to 91% of the series). No data about mortality were found. This study showed low frequency for MS in LAC compared with North American and European countries. The role of environmental and genetic factors should be well studied, providing new insights about its etiology.

scholarly journals Improving early epidemiological assessment of emerging Aedes-transmitted epidemics using historical data

2018 ◽  
Author(s):  
Julien Riou ◽  
Chiara Poletto ◽  
Pierre-Yves Boëlle
Keyword(s):  
Historical Data ◽  
A Priori ◽  
Total Duration ◽  
Reproduction Ratio ◽  
Peak Incidence ◽  
Incidence Data ◽  
Epidemic Size ◽  
Data Points ◽  
Final Epidemic Size ◽  
Reporting Rates

AbstractModel-based epidemiological assessment is useful to support decision-making at the beginning of an emerging Aedes-transmitted outbreak. However, early forecasts are generally unreliable as little information is available in the first few incidence data points. Here, we show how past Aedes-transmitted epidemics help improve these predictions. The approach was applied to the 2015-2017 Zika virus epidemics in three islands of the French West Indies, with historical data including other Aedes-transmitted diseases (Chikungunya and Zika) in the same and other locations. Hierarchical models were used to build informative a priori distributions on the reproduction ratio and the reporting rates. The accuracy and sharpness of forecasts improved substantially when these a priori distributions were used in models for prediction. For example, early forecasts of final epidemic size obtained without historical information were 3.3 times too high on average (range: 0.2 to 5.8) with respect to the eventual size, but were far closer (1.1 times the real value on average, range: 0.4 to 1.5) using information on past CHIKV epidemics in the same places. Likewise, the 97.5% upper bound for maximal incidence was 15.3 times (range: 2.0 to 63.1) the actual peak incidence, and became much sharper at 2.4 times (range: 1.3 to 3.9) the actual peak incidence with informative a priori distributions. Improvements were more limited for the date of peak incidence and the total duration of the epidemic. The framework can adapt to all forecasting models at the early stages of emerging Aedes-transmitted outbreaks.

scholarly journals Measles Outbreak in a Rural Population in Bahar District, Hamadan Province, West of Iran in 2018

2020 ◽  
Vol 20 (1) ◽  
pp. e00470-e00470
Author(s):  
Manoochehr Karami ◽  
Salman Khazaei ◽  
Seyed Mohsen Zahraei ◽  
Talat Mokhtari Azad ◽  
Ali Zahiri ◽  
...  
Keyword(s):  
Pearson Correlation ◽  
Epidemiological Data ◽  
Risk Groups ◽  
Reference Laboratory ◽  
Cross Sectional ◽  
Measles Outbreak ◽  
Significant Difference ◽  
History Of ◽  
Multistage Sampling ◽  
The Village

Background: Measles is in elimination phase in Iran. Following occurrence of two cases of measles in two children under six years old with the history of measles immunization in one of the villages affiliated to Bahar District, west of Hamadan Province, northwest of Iran, this study was conducted to determine the immunity status of people living in the village affected by the measles outbreak in spring of 2018. Study design: A cross-sectional (descriptive-analytical) study. Methods: Overall, 272 populations of 0-40 yr old in the village affected by outbreak were enrolled. Multistage sampling was used for choosing participants. The data collection tool was a researcher-made checklist used to collect epidemiological data including demographic characteristics and vaccination status. Blood samples were taken from them and sent to the measles reference laboratory for IgG testing. The amount of optical density (OD) greater than 0.2 was considered as positive and less than 0.1 negative, respectively. Results: The mean age of the study population was 17.4 ±11.8 yr and the sex ratio was almost the same. Levels of antibodies against measles were positive in 63.24%, intermediate in 12.5% and negative in 24.26%. There was no significant difference between the immunity status of the subjects with gender (P=0.236) and age group (P=0.113). Pearson correlation results showed that in males there was a significant positive correlation between the age of the participants and the serum IgG level (r = 0.26, P=0.003). Conclusion: Measles immunity in communities is not sufficient to prevent outbreaks and small epidemics, and it is recommended that periodically, serological assessments carried out at community level and especially at high-risk groups.

scholarly journals COVID-19 Epidemic Forecast in Different States of India using SIR Model

2020 ◽  
Author(s):  
Mukesh Jakhar ◽  
P K Ahluwalia ◽  
Ashok Kumar
Keyword(s):  
Tamil Nadu ◽  
Reproduction Number ◽  
Epidemiological Data ◽  
Final Phase ◽  
Statistical Parameters ◽  
Indian States ◽  
Infected People ◽  
Epidemic Curve ◽  
Epidemic Size ◽  
Final Epidemic Size

The epidemiological data up to 12th May 2020 for India and its 24 states has been used to predict COVID-19 outbreak within classical SIR (Susceptible-Infected-Recovered) model. The basic reproduction number R0 of India is calculated to be 1.15, whereas for various states it ranges from 1.03 in Uttarakhand to 7.92 in Bihar. The statistical parameters for most of the states indicates the high significance of the predicted results. It is estimated that the epidemic curve flattening in India will start from the first week of July and epidemic may end in the third week of October with final epidemic size ~1,75,000. The epidemic in Kerala is in final phase and is expected to end by first week of June. Among Indian states, Maharashtra is severely affected where the ending phase of epidemic may occur in the second week of September with epidemic size of ~55,000. The model indicates that the fast growth of infection in Punjab is from 27th April 2020 to 2nd June 2020, thereafter, curve flattening will start and the epidemic is expected to finished by the first week of July with the estimated number of ~3300 infected people. The epidemic size of COVID-19 outbreak in Delhi, West Bengal, Gujrat, Tamil Nadu and Odisha can reach as large as 24,000, 18,000, 16,000, 13,000 and 11,000, respectively, however, these estimations may be invalid if large fluctuation of data occurs in coming days.

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