Crowdsourcing data to mitigate epidemics

Leung, Gabriel M.; Leung, Kathy

doi:10.1016/s2589-7500(20)30055-8

Cited by 51 publications

(40 citation statements)

References 5 publications

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“…Evidently, such asymptomatic infectious cases are not fully reported by current testing method. However, some studies suggested crowdsourced data could be compiled and analyzed as an complementation of officially released data, which could perhaps help in improving the analysis results (27)(28)(29).…”

Section: Discussionmentioning

confidence: 99%

Evaluation of the Secondary Transmission Pattern and Epidemic Prediction of COVID-19 in the Four Metropolitan Areas of China

Hong

Zhou

et al. 2020

Front. Med.

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Understanding the transmission dynamics of COVID-19 is crucial for evaluating its spread pattern, especially in metropolitan areas of China, as its spread could lead to secondary outbreaks. In addition, the experiences gained and lessons learned from China have the potential to provide evidence to support other metropolitan areas and large cities outside China with their emerging cases. We used data reported from January 24, 2020, to February 23, 2020, to fit a model of infection, estimate the likely number of infections in four high-risk metropolitan areas based on the number of cases reported, and increase the understanding of the COVID-19 spread pattern. Considering the effect of the official quarantine regulations and travel restrictions for China, which began January 23∼24, 2020, we used the daily travel intensity index from the Baidu Maps app to roughly simulate the level of restrictions and estimate the proportion of the quarantined population. A group of SEIR model statistical parameters were estimated using Markov chain Monte Carlo (MCMC) methods and fitting on the basis of reported data. As a result, we estimated that the basic reproductive number, R 0 , was 2.91 in Beijing, 2.78 in Shanghai, 2.02 in Guangzhou, and 1.75 in Shenzhen based on the data from January 24, 2020, to February 23, 2020. In addition, we inferred the prediction results and compared the results of different levels of parameters. For example, in Beijing, the predicted peak number of cases was 467 with a peak time of March 01, 2020; however, if the city were to implement different levels (strict, moderate, or weak) of travel restrictions or regulation measures, the estimation results showed that the transmission dynamics would change and that the peak number of cases would differ by between 54% and 209%. We concluded that public health interventions would reduce the risk of the spread of COVID-19 and that more rigorous control and prevention measures would Su et al. Epidemic Prediction of the COVID-19 effectively contain its further spread, and awareness of prevention should be enhanced when businesses and social activities return to normal before the end of the epidemic. Further, the experiences gained and lessons learned from China offer the potential to provide evidence supporting other metropolitan areas and big cities with their emerging cases outside China.

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Section: Discussionmentioning

confidence: 99%

Evaluation of the Secondary Transmission Pattern and Epidemic Prediction of COVID-19 in the Four Metropolitan Areas of China

Hong

Zhou

et al. 2020

Front. Med.

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“…The spread of new pathogen severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused an expanding pandemic of coronavirus disease-2019 (COVID-19) (1)(2)(3)(4). As of March 10, 2020, the global number of confirmed cases of COVID-19 had surpassed 118 000 with more than 4292 deaths due to acute respiratory failure or other related complications (5).…”

Section: Introductionmentioning

confidence: 99%

Potential False-Negative Nucleic Acid Testing Results for Severe Acute Respiratory Syndrome Coronavirus 2 from Thermal Inactivation of Samples with Low Viral Loads

et al. 2020

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recommended to inactivate severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) before NAT. However, this procedure could theoretically disrupt nucleic acid integrity of this single-stranded RNA virus and cause false negatives in real-time polymerase chain reaction (RT-PCR) tests. METHODS:We investigated whether thermal inactivation could affect the results of viral NAT. We examined the effects of thermal inactivation on the quantitative RT-PCR results of SARS-CoV-2, particularly with regard to the rates of false-negative results for specimens carrying low viral loads. We additionally investigated the effects of different specimen types, sample preservation times, and a chemical inactivation approach on NAT. RESULTS:Our study showed increased Ct values in specimens from diagnosed COVID-19 patients in RT-PCR tests after thermal incubation. Moreover, about half of the weak-positive samples (7 of 15 samples, 46.7%) were RT-PCR negative after heat inactivation in at least one parallel testing. The use of guanidiniumbased lysis for preservation of these specimens had a smaller impact on RT-PCR results with fewer false negatives (2 of 15 samples, 13.3%) and significantly less increase in Ct values than heat inactivation. CONCLUSION: Thermal inactivation adversely affected the efficiency of RT-PCR for SARS-CoV-2 detection. Given the limited applicability associated with chemical inactivators, other approaches to ensure the overall protection of laboratory personnel need consideration.

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“…Therefore, on the one hand, we face the threats of a pandemic, which spreads in the absence of effective therapies and valid countermeasures and calls for major efforts to model and anticipate the time course of its diffusion 18 . On the other hand, we can speak of an infodemic threat 22 , which proliferates when credible information sources fail to capture the attention and trust of some parts of the public, for whom alternative, low-quality sources are more appealing as they capture more social attention 23 , better match their own beliefs or prejudices 24 , or sound more convincing, thanks to their typically straightforward messages 25 .…”

mentioning

confidence: 99%