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.
Dengue virus (DENV) infections may cause life-threatening dengue hemorrhagic fever (DHF).Suppressed protective immunity was shown in these patients. Although several hypotheses have been formulated, the mechanism of DENV-induced immunosuppression remains unclear. Previously, we found that cross-reactive antibodies against tumor necrosis factor-related apoptosis-inducing ligand (TRAIL) receptor 1 (death receptor 4 [DR4]) were elicited in DHF patients, and that anti-DR4 autoantibody fractions were elicited by nonstructural protein 1 (NS1) immunizations in experimental mice. In this study, we found that anti-DR4 antibodies could suppress B lymphocyte function in vitro and in vivo. Treatment with the anti-DR4 immunoglobulin (Ig) induced caspase-dependent cell death in immortalized B lymphocyte Raji cells in vitro. Anti-DR4 Igs elicited by NS1 and DR4 immunizations markedly suppressed mouse spleen transitional T2 B (IgM + IgD + ), bone marrow pre-pro-B (B220 + CD43 + ), pre-B (B220 + CD43 − ), and mature B cell (B220 + IgD + ) subsets in mice. Furthermore, functional analysis revealed that the pre-elicitation of anti-NS1 and anti-DR4 Ig titers suppressed subsequently neutralizing antibody production by immunization with DENV envelop protein. Our data suggest that the elicitation of anti-DR4 titers through DENV NS1 immunization plays a suppressive role in humoral immunity in mice.The dengue virus (DENV) is a mosquito-borne single positive-stranded RNA virus belonging to the Flaviviridae family (genus Flavivirus); the 4 major serotypes of DENV cause self-limiting dengue fever (DF) and life-threatening dengue hemorrhagic fever (DHF) 1 . It has been estimated that 50 million cases of DENV infections occur, and approximately 500,000 patients have been hospitalized with DHF, mainly in tropical and subtropical regions 2 . Evidence has suggested that because of the geographical extension of DENV infection and increases in the number of DENV cases and disease severity, DF and DHF have become major public health problems, with more than one-third of the global population residing in high-transmission-risk areas 3-5 . DHF is a complex disease, and its mechanism remains elusive. Currently, no specific treatment or effective vaccine is available for immunization cycle) in 1 wk intervals and then the bone marrow and spleen lymphocytes were analyzed 7 d later using aforementioned B cell markers. To analyze the potential suppressive effect of prior immunizations of NS1 on later induction of neutralizing antibody, C57BL/6J mice were first immunized with rGST, rNS1, rDR4 and rTACI by 2 immunization cycles (50 µg immunogen/mouse/immunization cycle) and then immunized with 2 additional immunization cycles of DENV rEIII in 1 wk intervals. The anti-EIII titer and the DENV-neutralizing property of these polyclonal antibody fractions were then analyzed.Statistical analyses. The means, standard deviations, and statistics for the quantifiable data were calculated using Microsoft Office Excel 2003, SigmaPlot 10 and SPSS 17. Significance of data...
The largest epidemic of avian influenza (AI) in history attacked poultry and wild birds throughout Taiwan starting January 6, 2015. This study analyzed surveillance results, epidemiologic characteristics, and viral sequences by using government-released information, with the intention to provide recommendations to minimize future pandemic influenza. The H5 clade 2.3.4.4 highly pathogenic AI viruses (HPAIVs) had not been detected in Taiwan before 2015. During this epidemic, four types of etiologic agents were identified: the three novel subtypes H5N2, H5N8, and H5N3 clade 2.3.4.4 HPAIVs and one endemic chicken H5N2 subtype (Mexican-like lineage) of low pathogenic AI viruses. Cocirculation of mixed subtypes also occurred, with H5N2 clade 2.3.4.4 HPAIVs accompanied by the H5N8 and H5N3 subtypes or old H5N2 viruses in the same farm. More than 90% of domestic geese died from this AI epidemic; geese were affected the most at the early outbreaks. The epidemic peaked in mid-January for all three novel H5 subtypes. Spatial epidemiology found that most affected areas were located in southwestern coastal areas. In terrestrial poultry (mostly chickens), different geographic distributions of AI virus subtypes were detected, with hot spots of H5N2 clade 2.3.4.4 vs. past-endemic old H5N2 viruses in Changhwa (P = 0.03) and Yunlin (P = 0.007) counties, respectively, of central Taiwan. Phylogenetic and sequence analyses of all the early 10 Taiwan H5 clade 2.3.4.4 isolates covering the three subtypes showed that they were very different from the HA of the past local H5 viruses from domestic ducks (75%-80%) and chickens (70%-75%). However, they had the highest sequence identity percentages (99.53%-100%), with the HA of A/crane/Kagoshima/KU13/2014(H5N8) isolated on December 7, 2014, in Japan being higher than those of recent American and Korean H5 HPAIVs [A/Northern pintail/Washington/40964/2014 (H5N2) and A/gyrfalcon/Washington/41088-6/2014 (H5N8): 99.02%-99.54% and A/Baikal teal/Korea/Donglim3/2014 (H5N8): 98.61%-99.08%], implying a likely common ancestor of these H5 clade 2.3.4.4 viruses. The multiple subtypes of H5 clade 2.3.4.4 HPAIVs imply high viral reassortment. We recommend establishing an integrated surveillance system, involving clinical, virologic, and serologic surveillance in poultry and wild birds, swine and other mammals prevalent on multiple-animal mixed-type traditional farms, and high-risk human populations, as a crucially important step to minimize future pandemic influenza.
SUMMARYWe analysed nation-wide reported measles cases during the 1988-9 epidemic and found that longer duration and wider spread were two major characteristics of the outbreak. All the 22 county/city index cases were reported with a delay of > 4 days and 64% were aged 5-14 years. This epidemic occurred mainly among 5-14-year-old school-children (59 % ), infants under 1 year (19%), and pre-school children ( 18 %). The overall attack rate was 0-63 cases per 10000 population, with the highest attack rate (7 4 cases per 10000 population) occurring in infants. Among 280 confirmed cases < 15 months of age, 9-month-old infants (42 cases) had a higher risk of measles and peaked at 10 months (49 cases). This epidemic started in March 1988 among 5-9-year-old children in the northern suburban area, then spread to Taipei City and neighbouring counties or cities. It continued to spread from the northern to southern and western areas during the summer vacation and New Year holidays. Multiple logistic regression analysis showed that the delay of measles reporting was strongly associated with the cases infected early in the epidemic (OR = 6-9, P < 0.001) and reported from teaching hospitals (OR = 2 6, P < 0-001). The reappearance of high attack rates among 5-9-year-old children in the 1985 and 1988-9 measles epidemics in Taiwan indicated the persistence of pockets of susceptible individuals even after mass immunization.
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