scholarly journals Deep reinforcement learning approaches for global public health strategies for COVID-19 pandemic

PLoS ONE ◽  
2021 ◽  
Vol 16 (5) ◽  
pp. e0251550
Author(s):  
Gloria Hyunjung Kwak ◽  
Lowell Ling ◽  
Pan Hui
Keyword(s):  
Public Health ◽  
Reinforcement Learning ◽  
Index Case ◽  
Health Interventions ◽  
Global Public Health ◽  
Public Health Interventions ◽  
Local Index ◽  
Travel Restrictions ◽  
Public Health Strategies ◽  
Travel Restriction

Background Unprecedented public health measures have been used during this coronavirus 2019 (COVID-19) pandemic to control the spread of SARS-CoV-2 virus. It is a challenge to implement timely and appropriate public health interventions. Methods and findings Population and COVID-19 epidemiological data between 21st January 2020 to 15th November 2020 from 216 countries and territories were included with the implemented public health interventions. We used deep reinforcement learning, and the algorithm was trained to enable agents to try to find optimal public health strategies that maximized total reward on controlling the spread of COVID-19. The results suggested by the algorithm were analyzed against the actual timing and intensity of lockdown and travel restrictions. Early implementations of the actual lockdown and travel restriction policies, usually at the time of local index case were associated with less burden of COVID-19. In contrast, our agent suggested to initiate at least minimal intensity of lockdown or travel restriction even before or on the day of the index case in each country and territory. In addition, the agent mostly recommended a combination of lockdown and travel restrictions and higher intensity policies than the policies implemented by governments, but did not always encourage rapid full lockdown and full border closures. The limitation of this study was that it was done with incomplete data due to the emerging COVID-19 epidemic, inconsistent testing and reporting. In addition, our research focuses only on population health benefits by controlling the spread of COVID-19 without balancing the negative impacts of economic and social consequences. Interpretation Compared to actual government implementation, our algorithm mostly recommended earlier intensity of lockdown and travel restrictions. Reinforcement learning may be used as a decision support tool for implementation of public health interventions during COVID-19 and future pandemics.

scholarly journals Modelling global public health strategies in COVID-19 pandemic using deep reinforcement learning

2020 ◽  
Author(s):  
Kwak Gloria Hyunjung ◽  
Lowell Ling ◽  
Pan Hui
Keyword(s):  
Public Health ◽  
Machine Learning ◽  
Reinforcement Learning ◽  
Health Interventions ◽  
Global Public Health ◽  
Public Health Interventions ◽  
Local Index ◽  
Travel Restrictions ◽  
Public Health Strategies ◽  
Travel Restriction

Abstract Rationale: Unprecedented public health measures have been used during this coronavirus 2019 (COVID-19) pandemic but with a cost to economic and social disruption. It is a challenge to implement timely and appropriate public health interventions.Objectives: This study evaluates the timing and intensity of public health policies in each country and territory in the COVID-19 pandemic, and whether machine learning can help them to find better global health strategies.Methods: Population and COVID-19 epidemiological data between 21st January 2020 to 7th April 2020 from 183 countries and 78 territories were included with the implemented public health interventions. We used deep reinforcement learning, and the model was trained to try to find the optimal public health strategies with maximizing total reward on controlling spread of COVID-19. The results proposed by the model were analyzed against the actual timing and intensity of lockdown and travel restrictions.Measurements and Main Results: Early implementation of the actual lockdown and travel restriction policies were associated with gradually groups of less severe crisis severity, relative to local index case date in each country or territory, not to 31st December 2019. However, our model suggested to initiate at least minimal intensity of lockdown or travel restriction even before index cases in each country and territory. In addition, the model mostly recommended a combination of lockdown and travel restrictions and higher intensity policies than the implemented policies by government, but did not always encourage rapid full lockdown and full border closures.Conclusion: Compared to actual government implementation, our model mostly recommended earlier and higher intensity of lockdown and travel restrictions. Machine learning may be used as a decision support tool for implementation of public health interventions during COVID-19 and future pandemics.

scholarly journals Dynamics of blaKPC-2 Dissemination from Non-CG258 Klebsiella pneumoniae to Other Enterobacterales via IncN Plasmids in an Area of High Endemicity

2020 ◽  
Vol 64 (12) ◽  
Author(s):  
Ana M. Rada ◽  
Elsa De La Cadena ◽  
Carlos Agudelo ◽  
Cesar Capataz ◽  
Nataly Orozco ◽  
...  
Keyword(s):  
Public Health ◽  
Klebsiella Pneumoniae ◽  
Neonatal Intensive Care ◽  
Carbapenem Resistance ◽  
Health Interventions ◽  
Global Public Health ◽  
Public Health Interventions ◽  
Content Type ◽  
Carbapenem Resistant ◽  
Long Read

ABSTRACT Carbapenem-resistant Enterobacterales (CRE) pose a significant threat to global public health. The most important mechanism for carbapenem resistance is the production of carbapenemases. Klebsiella pneumoniae carbapenemase (KPC) represents one of the main carbapenemases worldwide. Complex mechanisms of blaKPC dissemination have been reported in Colombia, a country with a high endemicity of carbapenem resistance. Here, we characterized the dynamics of dissemination of blaKPC gene among CRE infecting and colonizing patients in three hospitals localized in a highly endemic area of Colombia (2013 and 2015). We identified the genomic characteristics of KPC-producing Enterobacterales recovered from patients infected/colonized and reconstructed the dynamics of dissemination of blaKPC-2 using both short and long read sequencing. We found that spread of blaKPC-2 among Enterobacterales in the participating hospitals was due to intra- and interspecies horizontal gene transfer (HGT) mediated by promiscuous plasmids associated with transposable elements that was originated from a multispecies outbreak of KPC-producing Enterobacterales in a neonatal intensive care unit. The plasmids were detected in isolates recovered in other units within the same hospital and nearby hospitals. The gene “epidemic” was driven by IncN-pST15-type plasmids carrying a novel Tn4401b structure and non-Tn4401 elements (NTEKPC) in Klebsiella spp., Escherichia coli, Enterobacter spp., and Citrobacter spp. Of note, mcr-9 was found to coexist with blaKPC-2 in species of the Enterobacter cloacae complex. Our findings suggest that the main mechanism for dissemination of blaKPC-2 is HGT mediated by highly transferable plasmids among species of Enterobacterales in infected/colonized patients, presenting a major challenge for public health interventions in developing countries such as Colombia.

scholarly journals Modelling COVID-19 Transmission in the United States through Interstate and Foreign Travels and Evaluating Impact of Governmental Public Health Interventions

2020 ◽  
Author(s):  
Nita H. Shah ◽  
Nisha Sheoran ◽  
Ekta Jayswal ◽  
Dhairya Shukla ◽  
Nehal Shukla ◽  
...  
Keyword(s):  
Public Health ◽  
United States ◽  
Equilibrium Points ◽  
The United States ◽  
Health Interventions ◽  
United States Government ◽  
Optimal Controls ◽  
Public Health Interventions ◽  
Foreign Travel ◽  
Travel Restrictions

AbstractBackgroundThe first case of COVID-19 was reported in Wuhan, China in December 2019. The disease has spread to 210 countries and has been labelled as pandemic by WHO. Modelling, evaluating, and predicting the rate of disease transmission is crucial for epidemic prevention and control. Our aim is to assess the impact of interstate and foreign travel and public health interventions implemented by the United States government in response to the Covid-19 pandemic.MethodsA disjoint mutually exclusive compartmental model is developed to study transmission dynamics of the novel coronavirus. A system of non-linear differential equations was formulated and the basic reproduction number R0 was computed. Stability of the model was evaluated at the equilibrium points. Optimal controls were applied in the form of travel restrictions and quarantine. Numerical simulations were conducted.ResultsAnalysis shows that the model is locally asymptomatically stable, at endemic and foreigners free equilibrium points. Without any mitigation measures, infectivity and subsequent hospitalization of the population increases while placing interstates individuals and foreigners under quarantine, decreases the chances of exposure and subsequent infection, leading to an increase in the recovery rate.ConclusionInterstate and foreign travel restrictions, in addition to quarantine, help in effectively controlling the epidemic.

scholarly journals Genetic evidence for the association between COVID-19 epidemic severity and timing of non-pharmaceutical interventions

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Manon Ragonnet-Cronin ◽  
Olivia Boyd ◽  
Lily Geidelberg ◽  
David Jorgensen ◽  
Fabricia F. Nascimento ◽  
...  
Keyword(s):  
Public Health ◽  
Phylogenetic Analysis ◽  
Genetic Evidence ◽  
Health Interventions ◽  
Morbidity And Mortality ◽  
Whole Genome ◽  
Public Health Interventions ◽  
Epidemic Size ◽  
Travel Restrictions ◽  
Pharmaceutical Interventions

AbstractUnprecedented public health interventions including travel restrictions and national lockdowns have been implemented to stem the COVID-19 epidemic, but the effectiveness of non-pharmaceutical interventions is still debated. We carried out a phylogenetic analysis of more than 29,000 publicly available whole genome SARS-CoV-2 sequences from 57 locations to estimate the time that the epidemic originated in different places. These estimates were examined in relation to the dates of the most stringent interventions in each location as well as to the number of cumulative COVID-19 deaths and phylodynamic estimates of epidemic size. Here we report that the time elapsed between epidemic origin and maximum intervention is associated with different measures of epidemic severity and explains 11% of the variance in reported deaths one month after the most stringent intervention. Locations where strong non-pharmaceutical interventions were implemented earlier experienced much less severe COVID-19 morbidity and mortality during the period of study.

scholarly journals Non-Pharmaceutical Interventions in a Generalized Model of Interactive Dynamics Between COVID-19 and The Economy

2021 ◽  
Author(s):  
Soumya Datta ◽  
C. Saratchand
Keyword(s):  
Public Health ◽  
Health Interventions ◽  
Economic Activities ◽  
Public Health Interventions ◽  
Travel Restrictions ◽  
Functional Forms ◽  
Interactive Dynamics ◽  
The Impact ◽  
Pharmaceutical Interventions ◽  
Infection Recurrence

Abstract We use a simple general model of interactive dynamics between the COVID-19 pandemic and the economy to examine the impact of various non-pharmaceutical interventions in the form of restrictions on socio-economic activities like lockdowns, travel restrictions etc. We mathematically demonstrate that these restrictions might be useful in preventing repeated waves of infection recurrence in the pandemic. These results are general and not dependent on choice of specific functional forms or parameter configurations. We set out briefly the implications of these results for public health interventions.

scholarly journals Evolutionary Dynamics and Dissemination Pattern of the SARS-CoV-2 Lineage B.1.1.33 During the Early Pandemic Phase in Brazil

2021 ◽  
Vol 11 ◽  
Author(s):  
Paola Cristina Resende ◽  
Edson Delatorre ◽  
Tiago Gräf ◽  
Daiana Mir ◽  
Fernando Couto Motta ◽  
...  
Keyword(s):  
Public Health ◽  
Rio De Janeiro ◽  
Evolutionary Dynamics ◽  
Health Interventions ◽  
Reproductive Number ◽  
Public Health Interventions ◽  
Viral Genomes ◽  
Epidemic Dynamics ◽  
Travel Restrictions ◽  
Low Prevalence

A previous study demonstrates that most of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) Brazilian strains fell in three local clades that were introduced from Europe around late February 2020. Here we investigated in more detail the origin of the major and most widely disseminated SARS-CoV-2 Brazilian lineage B.1.1.33. We recovered 190 whole viral genomes collected from 13 Brazilian states from February 29 to April 31, 2020 and combined them with other B.1.1 genomes collected globally. Our genomic survey confirms that lineage B.1.1.33 is responsible for a variable fraction of the community viral transmissions in Brazilian states, ranging from 2% of all SARS-CoV-2 genomes from Pernambuco to 80% of those from Rio de Janeiro. We detected a moderate prevalence (5–18%) of lineage B.1.1.33 in some South American countries and a very low prevalence (<1%) in North America, Europe, and Oceania. Our study reveals that lineage B.1.1.33 evolved from an ancestral clade, here designated B.1.1.33-like, that carries one of the two B.1.1.33 synapomorphic mutations. The B.1.1.33-like lineage may have been introduced from Europe or arose in Brazil in early February 2020 and a few weeks later gave origin to the lineage B.1.1.33. These SARS-CoV-2 lineages probably circulated during February 2020 and reached all Brazilian regions and multiple countries around the world by mid-March, before the implementation of air travel restrictions in Brazil. Our phylodynamic analysis also indicates that public health interventions were partially effective to control the expansion of lineage B.1.1.33 in Rio de Janeiro because its median effective reproductive number (Re) was drastically reduced by about 66% during March 2020, but failed to bring it to below one. Continuous genomic surveillance of lineage B.1.1.33 might provide valuable information about epidemic dynamics and the effectiveness of public health interventions in some Brazilian states.

scholarly journals Spotlight on measles 2010: Preliminary report of an ongoing measles outbreak in a subpopulation with low vaccination coverage in Berlin, Germany, January-March 2010

2010 ◽  
Vol 15 (13) ◽  
Author(s):  
J Bätzing-Feigenbaum ◽  
U Pruckner ◽  
A Beyer ◽  
G Sinn ◽  
A Dinter ◽  
...  
Keyword(s):  
Public Health ◽  
Vaccination Coverage ◽  
School Children ◽  
Preliminary Report ◽  
Index Case ◽  
Health Interventions ◽  
Public Health Interventions ◽  
Measles Outbreak ◽  
Measles Incidence

Since early January 2010, Berlin has been experiencing a measles outbreak with 62 cases as of 31 March. The index case acquired the infection in India. In recent years, measles incidence in Berlin has been lower than the German average and vaccination coverage in school children has increased since 2001. However, this outbreak involves schools and kindergartens with low vaccination coverage and parents with critical attitudes towards vaccination, which makes the implementation of public health interventions challenging.

scholarly journals Evaluation of effects of public health interventions on COVID-19 transmission for Pakistan: A mathematical simulation study

2020 ◽  
Author(s):  
Zulfiqar A Bhutta ◽  
Ofir Harari ◽  
Jay JH Park ◽  
Noor-E Zannat ◽  
Michael Zoratti ◽  
...  
Keyword(s):  
Public Health ◽  
National Level ◽  
Public Health Intervention ◽  
Health Interventions ◽  
Exit Strategy ◽  
Public Health Interventions ◽  
Pakistani Population ◽  
Social Distancing ◽  
The Government ◽  
Public Health Strategies

AbstractBackgroundIn an effort to contain the COVID-19 epidemic, many governments across the world have enforced lockdown or social distancing measures. Several outbreak models have been developed to investigate the effects of different public health strategies for COVID-19, but they have not been developed for Pakistan and other South East Asian countries, where a large proportion of global population resides.MethodsWe developed a stochastic individual contact model by extending the widely-used Susceptible-Infectious-Recovered (SIR) compartment model with additional compartments to model both anticipated mitigating effects of public health intervention strategies for Pakistan. We estimated the projected spread, number of hospitalizations, and case fatalities under no intervention and four increasingly stringent public health strategies of social distancing and self-isolation at the national and provincial levels of Pakistan.ResultsOur analysis shows that without any public health interventions the expected number of cumulative case fatalities is 671,596 in Pakistan with the virus is expected to peak in terms of the number of required ICU-hospitalizations at 198,593 persons by the end of the June 2020. The estimated total numbers of cumulative case fatalities are lower for other public health strategies with strict social distancing showing the lowest number of deaths at 1,588 (Self-isolation: n=341,359; Flexible social distancing strategy: n=3,995; and Exit strategy: n=28,214). The lowest number of required ICU-hospitalization is also estimated for strict social distancing strategy (n=266 persons at the end of May 2020). Generally, the simulated effects of the different public health strategies at the provincial-level were similar to the national-level with strict social distancing showing the fewest number of case fatalities and ICU-hospitalizations.ConclusionOur results indicate that case fatalities and ICU-hospitalizations for Pakistan will be high without any public health interventions. While strict social distancing can potentially prevent a large number of deaths and ICU-hospitalizations, the government faces an important dilemma of potentially severe economic downfall. Consideration of a temporary strict social distancing strategy with gradual return of the lower-risk Pakistani population, as simulated in our exit strategy scenario, may an effective compromise between public health and economy of Pakistani population.

Sign in / Sign up

Export Citation Format

Share Document