During major epidemic outbreaks, demand for healthcare workers (HCWs) grows even as the extreme pressures they face cause declining availability. We draw on Taiwan’s severe acute respiratory syndrome (SARS) experience to argue that a modified form of traffic control bundling (TCB) protects HCW safety and by extension strengthens overall coronavirus disease 2019 (COVID-19) epidemic control.
In recent decades, the global incidence of dengue has increased. Affected countries have responded with more effective surveillance strategies to detect outbreaks early, monitor the trends, and implement prevention and control measures. We have applied newly developed machine learning approaches to identify laboratory-confirmed dengue cases from 4,894 emergency department patients with dengue-like illness (DLI) who received laboratory tests. Among them, 60.11% (2942 cases) were confirmed to have dengue. Using just four input variables [age, body temperature, white blood cells counts (WBCs) and platelets], not only the state-of-the-art deep neural network (DNN) prediction models but also the conventional decision tree (DT) and logistic regression (LR) models delivered performances with receiver operating characteristic (ROC) curves areas under curves (AUCs) of the ranging from 83.75% to 85.87% [for DT, DNN and LR: 84.60% ± 0.03%, 85.87% ± 0.54%, 83.75% ± 0.17%, respectively]. Subgroup analyses found all the models were very sensitive particularly in the pre-epidemic period. Pre-peak sensitivities (<35 weeks) were 92.6%, 92.9%, and 93.1% in DT, DNN, and LR respectively. Adjusted odds ratios examined with LR for low WBCs [≤ 3.2 (x103/μL)], fever (≥38°C), low platelet counts [< 100 (x103/μL)], and elderly (≥ 65 years) were 5.17 [95% confidence interval (CI): 3.96–6.76], 3.17 [95%CI: 2.74–3.66], 3.10 [95%CI: 2.44–3.94], and 1.77 [95%CI: 1.50–2.10], respectively. Our prediction models can readily be used in resource-poor countries where viral/serologic tests are inconvenient and can also be applied for real-time syndromic surveillance to monitor trends of dengue cases and even be integrated with mosquito/environment surveillance for early warning and immediate prevention/control measures. In other words, a local community hospital/clinic with an instrument of complete blood counts (including platelets) can provide a sentinel screening during outbreaks. In conclusion, the machine learning approach can facilitate medical and public health efforts to minimize the health threat of dengue epidemics. However, laboratory confirmation remains the primary goal of surveillance and outbreak investigation.
Quarantine for SARS during the 2003 Taiwan outbreak expedited case detection, thereby indirectly preventing infections.
The largest nosocomial outbreak of Middle East respiratory syndrome (MERS) occurred in South Korea in 2015. Health Care Personnel (HCP) are at high risk of acquiring MERS-Coronavirus (MERS-CoV) infections, similar to the severe acute respiratory syndrome (SARS)-Coronavirus (SARS-CoV) infections first identified in 2003. This study described the similarities and differences in epidemiological and clinical characteristics of 183 confirmed global MERS cases and 98 SARS cases in Taiwan associated with HCP. The epidemiological findings showed that the mean age of MERS-HCP and total MERS cases were 40 (24~74) and 49 (2~90) years, respectively, much older than those in SARS [SARS-HCP: 35 (21~68) years, p = 0.006; total SARS: 42 (0~94) years, p = 0.0002]. The case fatality rates (CFR) was much lower in MERS-HCP [7.03% (9/128)] or SARS-HCP [12.24% (12/98)] than the MERS-non-HCP [36.96% (34/92), p<0.001] or SARS-non-HCP [24.50% (61/249), p<0.001], however, no difference was found between MERS-HCP and SARS-HCP [p = 0.181]. In terms of clinical period, the days from onset to death [13 (4~17) vs 14.5 (0~52), p = 0.045] and to discharge [11 (5~24) vs 24 (0~74), p = 0.010] and be hospitalized days [9.5 (3~22) vs 22 (0~69), p = 0.040] were much shorter in MERS-HCP than SARS-HCP. Similarly, days from onset to confirmation were shorter in MERS-HCP than MERS-non-HCP [6 (1~14) vs 10 (1~21), p = 0.044]. In conclusion, the severity of MERS-HCP and SARS-HCP was lower than that of MERS-non-HCP and SARS-non-HCP due to younger age and early confirmation in HCP groups. However, no statistical difference was found in MERS-HCP and SARS-HCP. Thus, prevention of nosocomial infections involving both novel Coronavirus is crucially important to protect HCP.
During the 2003 Severe Acute Respiratory Syndrome (SARS) outbreak, traditional intervention measures such as quarantine and border control were found to be useful in containing the outbreak. We used laboratory verified SARS case data and the detailed quarantine data in Taiwan, where over 150,000 people were quarantined during the 2003 outbreak, to formulate a mathematical model which incorporates Level A quarantine (of potentially exposed contacts of suspected SARS patients) and Level B quarantine (of travelers arriving at borders from SARS affected areas) implemented in Taiwan during the outbreak. We obtain the average case fatality ratio and the daily quarantine rate for the Taiwan outbreak. Model simulations is utilized to show that Level A quarantine prevented approximately 461 additional SARS cases and 62 additional deaths, while the effect of Level B quarantine was comparatively minor, yielding only around 5% reduction of cases and deaths. The combined impact of the two levels of quarantine had reduced the case number and deaths by almost a half. The results demonstrate how modeling can be useful in qualitative evaluation of the impact of traditional intervention measures for newly emerging infectious diseases outbreak when there is inadequate information on the characteristics and clinical features of the new disease-measures which could become particularly important with the looming threat of global flu pandemic possibly caused by a novel mutating flu strain, including that of avian variety.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.