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1.
J Med Internet Res ; 22(9): e19907, 2020 09 09.
Article in English | MEDLINE | ID: covidwho-792453

ABSTRACT

BACKGROUND: The COVID-19 pandemic has caused major disruptions worldwide since March 2020. The experience of the 1918 influenza pandemic demonstrated that decreases in the infection rates of COVID-19 do not guarantee continuity of the trend. OBJECTIVE: The aim of this study was to develop a precise spread model of COVID-19 with time-dependent parameters via deep learning to respond promptly to the dynamic situation of the outbreak and proactively minimize damage. METHODS: In this study, we investigated a mathematical model with time-dependent parameters via deep learning based on forward-inverse problems. We used data from the Korea Centers for Disease Control and Prevention (KCDC) and the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University for Korea and the other countries, respectively. Because the data consist of confirmed, recovered, and deceased cases, we selected the susceptible-infected-recovered (SIR) model and found approximated solutions as well as model parameters. Specifically, we applied fully connected neural networks to the solutions and parameters and designed suitable loss functions. RESULTS: We developed an entirely new SIR model with time-dependent parameters via deep learning methods. Furthermore, we validated the model with the conventional Runge-Kutta fourth order model to confirm its convergent nature. In addition, we evaluated our model based on the real-world situation reported from the KCDC, the Korean government, and news media. We also crossvalidated our model using data from the CSSE for Italy, Sweden, and the United States. CONCLUSIONS: The methodology and new model of this study could be employed for short-term prediction of COVID-19, which could help the government prepare for a new outbreak. In addition, from the perspective of measuring medical resources, our model has powerful strength because it assumes all the parameters as time-dependent, which reflects the exact status of viral spread.


Subject(s)
Betacoronavirus , Coronavirus Infections/epidemiology , Deep Learning , Models, Theoretical , Neural Networks, Computer , Pandemics , Pneumonia, Viral/epidemiology , Humans , Mass Media , Republic of Korea/epidemiology , Time Factors
2.
F1000Res ; 9: 452, 2020.
Article in English | MEDLINE | ID: covidwho-776308

ABSTRACT

Background: Community containment is one of the common methods used to mitigate infectious disease outbreaks. The effectiveness of such a method depends on how strictly it is applied and the timing of its implementation. An early start and being strict is very effective; however, at the same time, it impacts freedom and economic opportunity. Here we created a simulation model to understand the effect of the starting day of community containment on the final outcome, that is, the number of those infected, hospitalized and those that died, as we followed the dynamics of COVID-19 pandemic. Methods: We used a stochastic recursive simulation method to apply disease outbreak dynamics measures of COVID-19 as an example to simulate disease spread. Parameters are allowed to be randomly assigned between higher and lower values obtained from published COVID-19 literature. Results: We simulated the dynamics of COVID-19 spread, calculated the number of active infections, hospitalizations and deaths as the outcome of our simulation and compared these results with real world data. We also represented the details of the spread in a network graph structure, and shared the code for the simulation model to be used for examining other variables. Conclusions: Early implementation of community containment has a big impact on the final outcome of an outbreak.


Subject(s)
Communicable Disease Control , Computer Simulation , Coronavirus Infections/prevention & control , Pandemics/prevention & control , Pneumonia, Viral/prevention & control , Time Factors , Betacoronavirus , Humans , Models, Theoretical
3.
PLoS One ; 15(9): e0238491, 2020.
Article in English | MEDLINE | ID: covidwho-771798

ABSTRACT

As the most visible face of health expertise to the general public, health agencies have played a central role in alerting the public to the emerging COVID-19 threat, providing guidance for protective action, motivating compliance with health directives, and combating misinformation. Social media platforms such as Twitter have been a critical tool in this process, providing a communication channel that allows both rapid dissemination of messages to the public at large and individual-level engagement. Message dissemination and amplification is a necessary precursor to reaching audiences, both online and off, as well as inspiring action. Therefore, it is valuable for organizational risk communication to identify strategies and practices that may lead to increased message passing among online users. In this research, we examine message features shown in prior disasters to increase or decrease message retransmission under imminent threat conditions to develop models of official risk communicators' messages shared online from February 1, 2020-April 30, 2020. We develop a lexicon of keywords associated with risk communication about the pandemic response, then use automated coding to identify message content and message structural features. We conduct chi-square analyses and negative binomial regression modeling to identify the strategies used by official risk communicators that respectively increase and decrease message retransmission. Findings show systematic changes in message strategies over time and identify key features that affect message passing, both positively and negatively. These results have the potential to aid in message design strategies as the pandemic continues, or in similar future events.


Subject(s)
Betacoronavirus , Communicable Diseases, Emerging , Communication , Coronavirus Infections , Information Dissemination/methods , Pandemics , Pneumonia, Viral , Social Media , Chi-Square Distribution , Emergencies , Emergency Medical Services/organization & administration , Government Agencies , Humans , Internet , Mass Media , Models, Statistical , Models, Theoretical , Public Health Administration , Safety Management , Social Media/statistics & numerical data
4.
PLoS One ; 15(9): e0238678, 2020.
Article in English | MEDLINE | ID: covidwho-771792

ABSTRACT

BACKGROUND: The COVID-19 virus pandemic has caused a significant number of deaths worldwide. If the prevalence of the infection continues to grow, this could impact life expectancy. This paper provides first estimates of the potential direct impact of the COVID-19 pandemic on period life expectancy. METHODS: From the estimates of bias-adjusted age-specific infection fatality rates in Hubei (China) and a range of six prevalence rate assumptions ranging from 1% to 70%, we built a discrete-time microsimulation model that simulates the number of people infected by COVID-19, the number dying from it, and the number of deaths from all causes week by week for a period of one year. We applied our simulation to four broad regions: North America and Europe; Latin America and the Caribbean; Southeastern Asia; and sub-Saharan African. For each region, 100,000 individuals per each 5-year age group are simulated. RESULTS: At a 10% COVID-19 prevalence rate, the loss in life expectancy at birth is likely above 1 year in North America and Europe and in Latin America and the Caribbean. In Southeastern Asia and sub-Saharan Africa, one year lost in life expectancy corresponds to an infection prevalence of about 15% and 25%, respectively. Given the uncertainty in fatality rates, with a 50% prevalence of COVID-19 infections under 95% prediction intervals, life expectancy would drop by 3 to 9 years in North America and Europe, by 3 to 8 years in Latin America and the Caribbean, by 2 to 7 years in Southeastern Asia, and by 1 to 4 years in sub-Saharan Africa. In all prevalence scenarios, as long as the COVID-19 infection prevalence rate remains below 1 or 2%, COVID-19 would not affect life expectancy in a substantial manner. INTERPRETATION: In regions with relatively high life expectancy, if the infection prevalence threshold exceeds 1 or 2%, the COVID-19 pandemic will break the secular trend of increasing life expectancy, resulting in a decline in period life expectancy. With life expectancy being a key indicator of human development, mortality increase, especially among the vulnerable subgroups of populations, would set a country back on its path of human development.


Subject(s)
Betacoronavirus , Coronavirus Infections/mortality , Life Expectancy , Pandemics , Pneumonia, Viral/mortality , Adult , Africa South of the Sahara/epidemiology , Age Distribution , Aged , Americas/epidemiology , Asia/epidemiology , Computer Simulation , Developing Countries , Europe/epidemiology , Female , Global Health , Humans , Male , Middle Aged , Models, Theoretical , Prevalence
5.
JMIR Mhealth Uhealth ; 8(7): e17216, 2020 07 09.
Article in English | MEDLINE | ID: covidwho-656023

ABSTRACT

BACKGROUND: Recent advancements in wearable sensor technology have shown the feasibility of remote physical therapy at home. In particular, the current COVID-19 pandemic has revealed the need and opportunity of internet-based wearable technology in future health care systems. Previous research has shown the feasibility of human activity recognition technologies for monitoring rehabilitation activities in home environments; however, few comprehensive studies ranging from development to clinical evaluation exist. OBJECTIVE: This study aimed to (1) develop a home-based rehabilitation (HBR) system that can recognize and record the type and frequency of rehabilitation exercises conducted by the user using a smartwatch and smartphone app equipped with a machine learning (ML) algorithm and (2) evaluate the efficacy of the home-based rehabilitation system through a prospective comparative study with chronic stroke survivors. METHODS: The HBR system involves an off-the-shelf smartwatch, a smartphone, and custom-developed apps. A convolutional neural network was used to train the ML algorithm for detecting home exercises. To determine the most accurate way for detecting the type of home exercise, we compared accuracy results with the data sets of personal or total data and accelerometer, gyroscope, or accelerometer combined with gyroscope data. From March 2018 to February 2019, we conducted a clinical study with two groups of stroke survivors. In total, 17 and 6 participants were enrolled for statistical analysis in the HBR group and control group, respectively. To measure clinical outcomes, we performed the Wolf Motor Function Test (WMFT), Fugl-Meyer Assessment of Upper Extremity, grip power test, Beck Depression Inventory, and range of motion (ROM) assessment of the shoulder joint at 0, 6, and 12 months, and at a follow-up assessment 6 weeks after retrieving the HBR system. RESULTS: The ML model created with personal data involving accelerometer combined with gyroscope data (5590/5601, 99.80%) was the most accurate compared with accelerometer (5496/5601, 98.13%) or gyroscope data (5381/5601, 96.07%). In the comparative study, the drop-out rates in the control and HBR groups were 40% (4/10) and 22% (5/22) at 12 weeks and 100% (10/10) and 45% (10/22) at 18 weeks, respectively. The HBR group (n=17) showed a significant improvement in the mean WMFT score (P=.02) and ROM of flexion (P=.004) and internal rotation (P=.001). The control group (n=6) showed a significant change only in shoulder internal rotation (P=.03). CONCLUSIONS: This study found that a home care system using a commercial smartwatch and ML model can facilitate participation in home training and improve the functional score of the WMFT and shoulder ROM of flexion and internal rotation in the treatment of patients with chronic stroke. This strategy can possibly be a cost-effective tool for the home care treatment of stroke survivors in the future. TRIAL REGISTRATION: Clinical Research Information Service KCT0004818; https://tinyurl.com/y92w978t.


Subject(s)
Home Care Services , Internet , Stroke Rehabilitation/methods , Stroke/physiopathology , Telerehabilitation , Upper Extremity/physiopathology , Wearable Electronic Devices , Aged , Chronic Disease , Exercise Therapy/statistics & numerical data , Humans , Machine Learning , Middle Aged , Mobile Applications , Models, Theoretical , Prospective Studies , Survivors , Treatment Outcome
6.
Lancet Glob Health ; 8(9): e1132-e1141, 2020 09.
Article in English | MEDLINE | ID: covidwho-641159

ABSTRACT

BACKGROUND: COVID-19 has the potential to cause substantial disruptions to health services, due to cases overburdening the health system or response measures limiting usual programmatic activities. We aimed to quantify the extent to which disruptions to services for HIV, tuberculosis, and malaria in low-income and middle-income countries with high burdens of these diseases could lead to additional loss of life over the next 5 years. METHODS: Assuming a basic reproduction number of 3·0, we constructed four scenarios for possible responses to the COVID-19 pandemic: no action, mitigation for 6 months, suppression for 2 months, or suppression for 1 year. We used established transmission models of HIV, tuberculosis, and malaria to estimate the additional impact on health that could be caused in selected settings, either due to COVID-19 interventions limiting activities, or due to the high demand on the health system due to the COVID-19 pandemic. FINDINGS: In high-burden settings, deaths due to HIV, tuberculosis, and malaria over 5 years could increase by up to 10%, 20%, and 36%, respectively, compared with if there was no COVID-19 pandemic. The greatest impact on HIV was estimated to be from interruption to antiretroviral therapy, which could occur during a period of high health system demand. For tuberculosis, the greatest impact would be from reductions in timely diagnosis and treatment of new cases, which could result from any prolonged period of COVID-19 suppression interventions. The greatest impact on malaria burden could be as a result of interruption of planned net campaigns. These disruptions could lead to a loss of life-years over 5 years that is of the same order of magnitude as the direct impact from COVID-19 in places with a high burden of malaria and large HIV and tuberculosis epidemics. INTERPRETATION: Maintaining the most critical prevention activities and health-care services for HIV, tuberculosis, and malaria could substantially reduce the overall impact of the COVID-19 pandemic. FUNDING: Bill & Melinda Gates Foundation, Wellcome Trust, UK Department for International Development, and Medical Research Council.


Subject(s)
Coronavirus Infections/epidemiology , Developing Countries , HIV Infections/prevention & control , Health Services Accessibility , Malaria/prevention & control , Pandemics , Pneumonia, Viral/epidemiology , Tuberculosis/prevention & control , HIV Infections/epidemiology , HIV Infections/mortality , Humans , Malaria/epidemiology , Malaria/mortality , Models, Theoretical , Tuberculosis/epidemiology , Tuberculosis/mortality
7.
F1000Res ; 9: 570, 2020.
Article in English | MEDLINE | ID: covidwho-769915

ABSTRACT

The 2019-2020 global pandemic has been caused by a disease called coronavirus disease 2019 (COVID-19). This disease has been caused by the Severe Acute Respiratory Syndrome coronavirus-2 (SARS-CoV-2). By April 30 2020, the World Health Organization reported 3,096,626 cases and 217,896 deaths, which implies an exponential growth for infection and deaths worldwide. Currently, there are various computer-based approaches that present COVID-19 data through different types of charts, which is very useful to recognise its behavior and trends. Nevertheless, such approaches do not allow for observation of any projection regarding confirmed cases and deaths, which would be useful to understand the trends of COVID-19. In this work, we have designed and developed an online dashboard that presents actual information about COVID-19. Furthermore, based on this information, we have designed a mathematical model in order to make projections about the evolution of cases and deaths worldwide and by country.


Subject(s)
Coronavirus Infections/mortality , Data Analysis , Pneumonia, Viral/mortality , Software , Betacoronavirus , Humans , Internet , Models, Theoretical , Pandemics
8.
F1000Res ; 9: 352, 2020.
Article in English | MEDLINE | ID: covidwho-769911

ABSTRACT

Background: School closures have been a recommended non-pharmaceutical intervention in pandemic response owing to the potential to reduce transmission of infection between children, school staff and those that they contact. However, given the many roles that schools play in society, closure for any extended period is likely to have additional impacts. Literature reviews of research exploring school closure to date have focused upon epidemiological effects; there is an unmet need for research that considers the multiplicity of potential impacts of school closures. Methods: We used systematic searching, coding and synthesis techniques to develop a systems-based logic model. We included literature related to school closure planned in response to epidemics large and small, spanning the 1918-19 'flu pandemic through to the emerging literature on the 2019 novel coronavirus. We used over 170 research studies and a number of policy documents to inform our model. Results: The model organises the concepts used by authors into seven higher level domains: children's health and wellbeing, children's education, impacts on teachers and other school staff, the school organisation, considerations for parents and families, public health considerations, and broader economic impacts. The model also collates ideas about potential moderating factors and ethical considerations. While dependent upon the nature of epidemics experienced to date, we aim for the model to provide a starting point for theorising about school closures in general, and as part of a wider system that is influenced by contextual and population factors. Conclusions: The model highlights that the impacts of school closures are much broader than those related solely to health, and demonstrates that there is a need for further concerted work in this area. The publication of this logic model should help to frame future research in this area and aid decision-makers when considering future school closure policy and possible mitigation strategies.


Subject(s)
Communicable Disease Control/methods , Coronavirus Infections/prevention & control , Influenza, Human/prevention & control , Pandemics/prevention & control , Pneumonia, Viral/prevention & control , Schools , Betacoronavirus , Disease Outbreaks/prevention & control , Humans , Models, Theoretical
9.
F1000Res ; 9: 232, 2020.
Article in English | MEDLINE | ID: covidwho-769909

ABSTRACT

Since the first identified case of COVID-19 in Wuhan, China, the disease has developed into a pandemic, imposing a major challenge for health authorities and hospitals worldwide. Mathematical transmission models can help hospitals to anticipate and prepare for an upcoming wave of patients by forecasting the time and severity of infections. Taking the city of Heidelberg as an example, we predict the ongoing spread of the disease for the next months including hospital and ventilator capacity and consider the possible impact of currently imposed countermeasures.


Subject(s)
Coronavirus Infections/epidemiology , Coronavirus Infections/transmission , Models, Theoretical , Pneumonia, Viral/epidemiology , Pneumonia, Viral/transmission , Betacoronavirus , Cities/epidemiology , Germany/epidemiology , Humans , Pandemics
10.
PLoS One ; 15(9): e0239251, 2020.
Article in English | MEDLINE | ID: covidwho-768843

ABSTRACT

This study quantifies the economic effect of a possible lockdown of Tokyo to prevent the spread of COVID-19. The negative effect of such a lockdown may propagate to other regions through supply chains because of supply and demand shortages. Applying an agent-based model to the actual supply chains of nearly 1.6 million firms in Japan, we simulate what would happen to production activities outside Tokyo if production activities that are not essential to citizens' survival in Tokyo were shut down for a certain period. We find that if Tokyo were locked down for a month, the indirect effect on other regions would be twice as large as the direct effect on Tokyo, leading to a total production loss of 27 trillion yen in Japan or 5.2% of the country's annual GDP. Although the production that would be shut down in Tokyo accounts for 21% of the total production in Japan, the lockdown would result in an 86% reduction of the daily production in Japan after one month.


Subject(s)
Coronavirus Infections/prevention & control , Economic Recession , Pandemics/prevention & control , Pneumonia, Viral/prevention & control , Betacoronavirus/isolation & purification , Coronavirus Infections/pathology , Coronavirus Infections/virology , Humans , Japan , Models, Theoretical , Pneumonia, Viral/pathology , Pneumonia, Viral/virology , Tokyo
11.
Int J Environ Res Public Health ; 17(18)2020 09 08.
Article in English | MEDLINE | ID: covidwho-760916

ABSTRACT

The self-organizing mechanism is a universal approach that is widely followed in nature. In this work, a novel self-organizing model describing diffusion over a lattice is introduced. Simulation results for the model's active lattice sites demonstrate an evolution curve that is very close to those describing the evolution of infected European populations by COVID-19. The model was further examined against real data regarding the COVID-19 epidemic for seven European countries (with a total population of 290 million) during the periods in which social distancing measures were imposed, namely Italy and Spain, which had an enormous spread of the disease; the successful case of Greece; and four central European countries: France, Belgium, Germany and the Netherlands. The value of the proposed model lies in its simplicity and in the fact that it is based on a universal natural mechanism, which through the presentation of an equivalent dynamical system apparently documents and provides a better understanding of the dynamical process behind viral epidemic spreads in general-even pandemics, such as in the case of COVID-19-further allowing us to come closer to controlling such situations. Finally, this model allowed the study of dynamical characteristics such as the memory effect, through the autocorrelation function, in the studied epidemiological dynamical systems.


Subject(s)
Betacoronavirus , Coronavirus Infections/epidemiology , Pandemics , Pneumonia, Viral/epidemiology , Europe/epidemiology , Humans , Models, Theoretical , Physics
12.
PLoS One ; 15(9): e0238412, 2020.
Article in English | MEDLINE | ID: covidwho-760696

ABSTRACT

We investigate phase transitions associated with three control methods for epidemics on small world networks. Motivated by the behavior of SARS-CoV-2, we construct a theoretical SIR model of a virus that exhibits presymptomatic, asymptomatic, and symptomatic stages in two possible pathways. Using agent-based simulations on small world networks, we observe phase transitions for epidemic spread related to: 1) Global social distancing with a fixed probability of adherence. 2) Individually initiated social isolation when a threshold number of contacts are infected. 3) Viral shedding rate. The primary driver of total number of infections is the viral shedding rate, with probability of social distancing being the next critical factor. Individually initiated social isolation was effective when initiated in response to a single infected contact. For each of these control measures, the total number of infections exhibits a sharp phase transition as the strength of the measure is varied.


Subject(s)
Coronavirus Infections/transmission , Models, Theoretical , Pneumonia, Viral/transmission , Asymptomatic Diseases , Betacoronavirus/isolation & purification , Betacoronavirus/physiology , Coronavirus Infections/epidemiology , Coronavirus Infections/pathology , Coronavirus Infections/virology , Epidemics , Humans , Pandemics , Pneumonia, Viral/epidemiology , Pneumonia, Viral/pathology , Pneumonia, Viral/virology , Virus Shedding
13.
J Transl Med ; 18(1): 345, 2020 09 05.
Article in English | MEDLINE | ID: covidwho-745680

ABSTRACT

BACKGROUND: Coronavirus disease 2019 (COVID-19), which is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), spreads rapidly and has attracted worldwide attention. METHODS: To improve the forecast accuracy and investigate the spread of SARS-CoV-2, we constructed four mathematical models to numerically estimate the spread of SARS-CoV-2 and the efficacy of eradication strategies. RESULTS: Using the Susceptible-Exposed-Infected-Removed (SEIR) model, and including measures such as city closures and extended leave policies implemented by the Chinese government that effectively reduced the ß value, we estimated that the ß value and basic transmission number, R0, of SARS-CoV-2 was 0.476/6.66 in Wuhan, 0.359/5.03 in Korea, and 0.400/5.60 in Italy. Considering medicine and vaccines, an advanced model demonstrated that the emergence of vaccines would greatly slow the spread of the virus. Our model predicted that 100,000 people would become infected assuming that the isolation rate α in Wuhan was 0.30. If quarantine measures were taken from March 10, 2020, and the quarantine rate of α was also 0.3, then the final number of infected people was predicted to be 11,426 in South Korea and 147,142 in Italy. CONCLUSIONS: Our mathematical models indicate that SARS-CoV-2 eradication depends on systematic planning, effective hospital isolation, and SARS-CoV-2 vaccination, and some measures including city closures and leave policies should be implemented to ensure SARS-CoV-2 eradication.


Subject(s)
Betacoronavirus/physiology , Coronavirus Infections/prevention & control , Coronavirus Infections/virology , Disease Eradication , Models, Theoretical , Pandemics/prevention & control , Pneumonia, Viral/prevention & control , Pneumonia, Viral/virology , China/epidemiology , Coronavirus Infections/epidemiology , Epidemics/prevention & control , Government , Humans , Italy/epidemiology , Pneumonia, Viral/epidemiology , Quarantine , Republic of Korea/epidemiology , Vaccination
14.
J Korean Med Sci ; 35(35): e321, 2020 Sep 07.
Article in English | MEDLINE | ID: covidwho-745664

ABSTRACT

BACKGROUND: The coronavirus disease 2019 (COVID-19) pandemic has posed significant global public health challenges and created a substantial economic burden. Korea has experienced an extensive outbreak, which was linked to a religion-related super-spreading event. However, the implementation of various non-pharmaceutical interventions (NPIs), including social distancing, spring semester postponing, and extensive testing and contact tracing controlled the epidemic. Herein, we estimated the effectiveness of each NPI using a simulation model. METHODS: A compartment model with a susceptible-exposed-infectious-quarantined-hospitalized structure was employed. Using the Monte-Carlo-Markov-Chain algorithm with Gibbs' sampling method, we estimated the time-varying effective contact rate to calibrate the model with the reported daily new confirmed cases from February 12th to March 31st (7 weeks). Moreover, we conducted scenario analyses by adjusting the parameters to estimate the effectiveness of NPI. RESULTS: Relaxed social distancing among adults would have increased the number of cases 27.4-fold until the end of March. Spring semester non-postponement would have increased the number of cases 1.7-fold among individuals aged 0-19, while lower quarantine and detection rates would have increased the number of cases 1.4-fold. CONCLUSION: Among the three NPI measures, social distancing in adults showed the highest effectiveness. The substantial effect of social distancing should be considered when preparing for the 2nd wave of COVID-19.


Subject(s)
Communicable Disease Control/methods , Contact Tracing/methods , Coronavirus Infections/transmission , Mass Screening/methods , Pneumonia, Viral/transmission , Social Distance , Betacoronavirus , Computer Simulation , Environmental Exposure/prevention & control , Humans , Markov Chains , Models, Theoretical , Monte Carlo Method , Pandemics , Public Health Practice/legislation & jurisprudence , Republic of Korea
15.
PLoS One ; 15(9): e0238559, 2020.
Article in English | MEDLINE | ID: covidwho-745053

ABSTRACT

The novel coronavirus (SARS-CoV-2), identified in China at the end of December 2019 and causing the disease COVID-19, has meanwhile led to outbreaks all over the globe with about 2.2 million confirmed cases and more than 150,000 deaths as of April 17, 2020. In this work, mathematical models are used to reproduce data of the early evolution of the COVID-19 outbreak in Germany, taking into account the effect of actual and hypothetical non-pharmaceutical interventions. Systems of differential equations of SEIR type are extended to account for undetected infections, stages of infection, and age groups. The models are calibrated on data until April 5. Data from April 6 to 14 are used for model validation. We simulate different possible strategies for the mitigation of the current outbreak, slowing down the spread of the virus and thus reducing the peak in daily diagnosed cases, the demand for hospitalization or intensive care units admissions, and eventually the number of fatalities. Our results suggest that a partial (and gradual) lifting of introduced control measures could soon be possible if accompanied by further increased testing activity, strict isolation of detected cases, and reduced contact to risk groups.


Subject(s)
Coronavirus Infections/epidemiology , Models, Theoretical , Pneumonia, Viral/epidemiology , Adolescent , Adult , Age Factors , Aged , Aged, 80 and over , Child , Child, Preschool , Communicable Disease Control/methods , Communicable Disease Control/statistics & numerical data , Coronavirus Infections/prevention & control , Coronavirus Infections/transmission , Disease Transmission, Infectious/statistics & numerical data , Germany/epidemiology , Hospitalization/statistics & numerical data , Humans , Infant , Middle Aged , Pandemics/prevention & control , Pneumonia, Viral/prevention & control , Pneumonia, Viral/transmission
16.
Sensors (Basel) ; 20(17)2020 Sep 02.
Article in English | MEDLINE | ID: covidwho-742835

ABSTRACT

COVID-19 has shown a relatively low case fatality rate in young healthy individuals, with the majority of this group being asymptomatic or having mild symptoms. However, the severity of the disease among the elderly as well as in individuals with underlying health conditions has caused significant mortality rates worldwide. Understanding this variance amongst different sectors of society and modelling this will enable the different levels of risk to be determined to enable strategies to be applied to different groups. Long-established compartmental epidemiological models like SIR and SEIR do not account for the variability encountered in the severity of the SARS-CoV-2 disease across different population groups. The objective of this study is to investigate how a reduction in the exposure of vulnerable individuals to COVID-19 can minimise the number of deaths caused by the disease, using the UK as a case study. To overcome the limitation of long-established compartmental epidemiological models, it is proposed that a modified model, namely SEIR-v, through which the population is separated into two groups regarding their vulnerability to SARS-CoV-2 is applied. This enables the analysis of the spread of the epidemic when different contention measures are applied to different groups in society regarding their vulnerability to the disease. A Monte Carlo simulation (100,000 runs) along the proposed SEIR-v model is used to study the number of deaths which could be avoided as a function of the decrease in the exposure of vulnerable individuals to the disease. The results indicate a large number of deaths could be avoided by a slight realistic decrease in the exposure of vulnerable groups to the disease. The mean values across the simulations indicate 3681 and 7460 lives could be saved when such exposure is reduced by 10% and 20% respectively. From the encouraging results of the modelling a number of mechanisms are proposed to limit the exposure of vulnerable individuals to the disease. One option could be the provision of a wristband to vulnerable people and those without a smartphone and contact-tracing app, filling the gap created by systems relying on smartphone apps only. By combining very dense contact tracing data from smartphone apps and wristband signals with information about infection status and symptoms, vulnerable people can be protected and kept safer.


Subject(s)
Coronavirus Infections/prevention & control , Coronavirus Infections/transmission , Disease Transmission, Infectious/statistics & numerical data , Models, Theoretical , Pandemics/prevention & control , Pneumonia, Viral/prevention & control , Pneumonia, Viral/transmission , Public Health/methods , Quarantine/organization & administration , Vulnerable Populations , Contact Tracing/methods , Coronavirus Infections/epidemiology , Disease Outbreaks/prevention & control , Health Planning Guidelines , Health Services Needs and Demand , Humans , Infection Control/methods , Infection Control/organization & administration , Infection Control/statistics & numerical data , Inventions/statistics & numerical data , Pneumonia, Viral/epidemiology , Preventive Health Services/methods , Preventive Health Services/organization & administration , Preventive Health Services/standards , Public Health/statistics & numerical data , Public Health Administration/methods , Quarantine/methods , Quarantine/statistics & numerical data , United Kingdom/epidemiology , Vulnerable Populations/statistics & numerical data
17.
Rev Peru Med Exp Salud Publica ; 37(2): 195-202, 2020.
Article in Spanish, English | MEDLINE | ID: covidwho-740608

ABSTRACT

OBJECTIVES: To determine the probability of controlling the outbreak of COVID-19 in Peru, in a pre- and post-quarantine scenario using mathematical simulation models. MATERIALS AND METHODS: Outbreak si mulations for the COVID-19 pandemic are performed, using stochastic equations under the following assumptions: a pre-quarantine population R0 of 2.7 or 3.5, a post-quarantine R0 of 1.5, 2 or 2.7, 18% or 40%, of asymptomatic positives and a maximum response capacity of 50 or 150 patients in the intensive care units. The success of isolation and contact tracing is evaluated, no other mitigation measures are included. RESULTS: In the pre-quarantine stage, success in controlling more than 80% of the simulations occurred only if the isolation of positive cases was implemented from the first case, after which there was less than 40% probability of success. In post-quarantine, with 60 positive cases it is necessary to isolate them early, track all of their contacts and decrease the R0 to 1.5 for outbreak control to be successful in more than 80% of cases. Other scenarios have a low probability of success. CONCLUSIONS: The control of the outbreak in Peru during pre-quarantine stage demanded requirements that were difficult to comply with, therefore quarantine was necessary; to successfully suspend it would require a significant reduction in the spread of the disease, early isolation of positives and follow-up of all contacts of positive patients.


Subject(s)
Computer Simulation , Coronavirus Infections/epidemiology , Disease Outbreaks/prevention & control , Pneumonia, Viral/epidemiology , Contact Tracing/methods , Coronavirus Infections/prevention & control , Humans , Intensive Care Units/statistics & numerical data , Models, Theoretical , Pandemics/prevention & control , Peru/epidemiology , Pneumonia, Viral/prevention & control , Probability , Quarantine
18.
J Med Internet Res ; 22(9): e19907, 2020 09 09.
Article in English | MEDLINE | ID: covidwho-740477

ABSTRACT

BACKGROUND: The COVID-19 pandemic has caused major disruptions worldwide since March 2020. The experience of the 1918 influenza pandemic demonstrated that decreases in the infection rates of COVID-19 do not guarantee continuity of the trend. OBJECTIVE: The aim of this study was to develop a precise spread model of COVID-19 with time-dependent parameters via deep learning to respond promptly to the dynamic situation of the outbreak and proactively minimize damage. METHODS: In this study, we investigated a mathematical model with time-dependent parameters via deep learning based on forward-inverse problems. We used data from the Korea Centers for Disease Control and Prevention (KCDC) and the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University for Korea and the other countries, respectively. Because the data consist of confirmed, recovered, and deceased cases, we selected the susceptible-infected-recovered (SIR) model and found approximated solutions as well as model parameters. Specifically, we applied fully connected neural networks to the solutions and parameters and designed suitable loss functions. RESULTS: We developed an entirely new SIR model with time-dependent parameters via deep learning methods. Furthermore, we validated the model with the conventional Runge-Kutta fourth order model to confirm its convergent nature. In addition, we evaluated our model based on the real-world situation reported from the KCDC, the Korean government, and news media. We also crossvalidated our model using data from the CSSE for Italy, Sweden, and the United States. CONCLUSIONS: The methodology and new model of this study could be employed for short-term prediction of COVID-19, which could help the government prepare for a new outbreak. In addition, from the perspective of measuring medical resources, our model has powerful strength because it assumes all the parameters as time-dependent, which reflects the exact status of viral spread.


Subject(s)
Betacoronavirus , Coronavirus Infections/epidemiology , Deep Learning , Models, Theoretical , Neural Networks, Computer , Pandemics , Pneumonia, Viral/epidemiology , Humans , Mass Media , Republic of Korea/epidemiology , Time Factors
19.
PLoS One ; 15(9): e0237627, 2020.
Article in English | MEDLINE | ID: covidwho-740402

ABSTRACT

The ongoing COVID-19 epidemics poses a particular challenge to low and middle income countries, making some of them consider the strategy of "vertical confinement". In this strategy, contact is reduced only to specific groups (e.g. age groups) that are at increased risk of severe disease following SARS-CoV-2 infection. We aim to assess the feasibility of this scenario as an exit strategy for the current lockdown in terms of its ability to keep the number of cases under the health care system capacity. We developed a modified SEIR model, including confinement, asymptomatic transmission, quarantine and hospitalization. The population is subdivided into 9 age groups, resulting in a system of 72 coupled nonlinear differential equations. The rate of transmission is dynamic and derived from the observed delayed fatality rate; the parameters of the epidemics are derived with a Markov chain Monte Carlo algorithm. We used Brazil as an example of middle income country, but the results are easily generalizable to other countries considering a similar strategy. We find that starting from 60% horizontal confinement, an exit strategy on May 1st of confinement of individuals older than 60 years old and full release of the younger population results in 400 000 hospitalizations, 50 000 ICU cases, and 120 000 deaths in the 50-60 years old age group alone. Sensitivity analysis shows the 95% confidence interval brackets a order of magnitude in cases or three weeks in time. The health care system avoids collapse if the 50-60 years old are also confined, but our model assumes an idealized lockdown where the confined are perfectly insulated from contamination, so our numbers are a conservative lower bound. Our results discourage confinement by age as an exit strategy.


Subject(s)
Coronavirus Infections/pathology , Models, Theoretical , Pneumonia, Viral/pathology , Age Factors , Betacoronavirus/isolation & purification , Brazil/epidemiology , Coronavirus Infections/epidemiology , Coronavirus Infections/transmission , Coronavirus Infections/virology , Humans , Markov Chains , Monte Carlo Method , Pandemics , Pneumonia, Viral/epidemiology , Pneumonia, Viral/transmission , Pneumonia, Viral/virology , Quarantine
20.
Int J Environ Res Public Health ; 17(17)2020 08 27.
Article in English | MEDLINE | ID: covidwho-738631

ABSTRACT

In Japan's response to the coronavirus disease 2019 (COVID-19), virus testing was limited to symptomatic patients due to limited capacity, resulting in uncertainty regarding the spread of infection and the appropriateness of countermeasures. System dynamic modelling, comprised of stock flow and infection modelling, was used to describe regional population dynamics and estimate assumed region-specific transmission rates. The estimated regional transmission rates were then mapped against actual patient data throughout the course of the interventions. This modelling, together with simulation studies, demonstrated the effectiveness of inbound traveler quarantine and resident self-isolation policies and practices. A causal loop approach was taken to link societal factors to infection control measures. This causal loop modelling suggested that the only effective measure against COVID-19 transmission in the Japanese context was intervention in the early stages of the outbreak by national and regional governments, and no social self-strengthening dynamics were demonstrated. These findings may contribute to an understanding of how social resilience to future infectious disease threats can be developed.


Subject(s)
Communicable Disease Control/methods , Coronavirus Infections/prevention & control , Pandemics/prevention & control , Pneumonia, Viral/prevention & control , Betacoronavirus , Coronavirus Infections/transmission , Humans , Japan , Models, Theoretical , Pneumonia, Viral/transmission , Quarantine , Social Isolation
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