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Background: Sentinel physician surveillance in communities has played an important role in detecting early signs of epidemics. The traditional approach is to let the primary care physician voluntarily and actively report diseases to the health department on a weekly basis. However, this is labor-intensive work, and the spatio-temporal resolution of the surveillance data is not precise at all. In this study, we built up a clinic-based enhanced sentinel surveillance system named "Sentinel plus" which was designed for sentinel clinics and community hospitals to monitor 23 kinds of syndromic groups in Taipei City, Taiwan. The definitions of those syndromic groups were based on ICD-10 diagnoses from physicians., Methods: Daily ICD-10 counts of two syndromic groups including ILI and EV-like syndromes in Taipei City were extracted from Sentinel plus. A negative binomial regression model was used to couple with lag structure functions to examine the short-term association between ICD counts and meteorological variables. After fitting the negative binomial regression model, residuals were further rescaled to Pearson residuals. We then monitored these daily standardized Pearson residuals for any aberrations from July 2018 to October 2019., Results: The results showed that daily average temperature was significantly negatively associated with numbers of ILI syndromes. The ozone and PM2.5 concentrations were significantly positively associated with ILI syndromes. In addition, daily minimum temperature, and the ozone and PM2.5 concentrations were significantly negatively associated with the EV-like syndromes. The aberrational signals detected from clinics for ILI and EV-like syndromes were earlier than the epidemic period based on outpatient surveillance defined by the Taiwan CDC., Conclusions: This system not only provides warning signals to the local health department for managing the risks but also reminds medical practitioners to be vigilant toward susceptible patients. The near real-time surveillance can help decision makers evaluate their policy on a timely basis., Competing Interests: The authors have declared that no competing interests exist.

Background: Epidemics of hand, foot and mouth disease (HFMD) among children in East Asia have been a serious annual public health problem. Previous studies in China and island-type territories in East Asia showed that the onset of HFMD epidemics evolved with increased latitude. Based on the natural characteristics of the epidemics, we developed regression models for issuing aberration alerts and predictions., Methods: HFMD sentinel surveillance data from 2008 to 2014 in Japan are used in this study, covering 365 weeks and 47 prefectures between 24 and 46° of north latitude. Average HFMD cases per sentinel are standardized as Z rates. We fit weekly Z rate differences between prefectures located in the south and north of a designated prefecture with linear regression models to detect the surging trend of the epidemic for the prefecture. We propose a rule for issuing an aberration alert determined by the strength of the upward trend of south-north Z rate differences in the previous few weeks. In addition to the warning, we predict a Z rate for the next week with a 95% confidence interval., Results: We selected Tokyo and Kyoto for evaluating the proposed approach to aberration detection. Overall, the peaks of epidemics in Tokyo mostly occurred in weeks 28-31, later than in Kyoto, where the disease peaked in weeks 26-31. Positive south-north Z rate differences in both prefectures were clearly observed ahead of the HFMD epidemic cycles. Aberrations in the major epidemics of 2011 and 2013 were successfully detected weeks earlier. The prediction also provided accurate estimates of the epidemic's trends., Conclusions: We have used only the latitude, one geographical feature affecting the spatiotemporal distribution of HFMD, to develop rules for early aberration detection and prediction. We have also demonstrated that the proposed rules performed well using real data in terms of accuracy and timeliness. Although our approach may provide helpful information for controlling epidemics and minimizing the impact of diseases, the performance could be further improved by including other influential meteorological factors in the proposed latitude-based approach, which is worth further investigation.

Strict and repeated lockdowns have caused public fatigue regarding policy compliance and had a large impact on several countries' economies. We aimed to evaluate the effectiveness of a soft lockdown policy and the strategy of active community screening for controlling COVID-19 in Taiwan. We used village-based daily confirmed COVID-19 statistics in Taipei City and New Taipei City, between May 2, 2021, and July 17, 2021. The temporal Gi* statistic was used to compute the spatiotemporal hotspots. Simple linear regression was used to evaluate the trend of the epidemic, positivity rate from community screening, and mobility changes in COVID-19 cases and incidence before and after a level three alert in both cities. We used a Bayesian hierarchical zero-inflated Poisson model to estimate the daily infection risk. The cities accounted for 11,403 (81.17%) of 14,048 locally confirmed cases. The mean effective reproduction number (Re) surged before the level three alert and peaked on May 16, 2021, the day after the level three alert in Taipei City (Re = 3.66) and New Taipei City (Re = 3.37). Mobility reduction and a lower positive rate were positively associated with a lower number of cases and incidence. In the spatiotemporal view, seven major districts were identified with a radial spreading pattern from one hard-hit district. Villages with a higher inflow degree centrality among people aged ≥ 60 years, having confirmed cases, specific land-use types, and with a higher aging index had higher infection risks than other villages. Early soft lockdown policy and detection of infected patients showed an effective strategy to control COVID-19 in Taiwan. [ABSTRACT FROM AUTHOR]

During 2012–2017, a total of 1,144 highly pathogenic avian influenza (HPAI) H5 outbreaks were reported in Taiwan. We conjectured the current 3‐km radius of the post‐outbreak containment policy could fail to effectively alleviate the current ongoing epidemics of HPAI H5 in Taiwan. The high intensity of localized transmission of HPAI H5 at certain focal hotspots was identified to follow the spatial distribution of poultry‐raising locations through our hotspot analyses on the HPAI H5 outbreak locations from 2015 to 2017. We then applied 3‐, 5‐ and 7‐km circular buffer zones to 15,444 registered poultry‐raising locations to inspect the characteristics of the poultry‐raising neighbourhood. Three spatial regression models using Bayesian inference were established to infer the risks attributable to poultry‐raising characteristics in the corresponding buffer areas. The different buffer radii were treated as a sensitivity analysis of the influential range of neighbouring farms on the HPAI H5 outbreak occurrence, so as to evaluate the effective radius for post‐outbreak containment. Evidence showed that the risks of outbreak occurrence were associated with increasing numbers of poultry‐raising locations in both 3‐km (relative risk [RR] 1.005, 95% confidence interval [CI] 1.002–1.008) and 5‐km buffer areas (RR 1.005, 95% CI 1.004–1.007), whereas in the 7‐km buffer model, no association between densely populated locations and increasing risks of outbreaks was observed (RR 1.000, 95% CI 0.999–1.001). Therefore, an extension to a 7‐km radius for the post‐outbreak containment policy (rather than a 3‐km radius as in the current policy) is recommended to effectively mitigate further spreading of HPAI H5 outbreaks among neighbouring farms. Overall, we demonstrated that the densely populated locations with multiple poultry species raised in proximity as defined with 3‐, 5‐ and 7‐km buffer areas facilitated H5 HPAI outbreak diffusion and shaped the scale of HPAI H5 epidemics in Taiwan. [ABSTRACT FROM AUTHOR]

Background: A re-emergence of scarlet fever has been noted in Hong Kong, South Korea, and England, UK, since 2008. China also had a sudden increase in the incidence of the disease in 2011. In this study, we aimed to assess the epidemiological changes before and after the upsurge. We also aimed to explore the reasons for the upsurge in disease in 2011, the epidemiological factors that contributed to it, and assess how these could be managed to prevent future epidemics.Methods: In this observational study, we extracted the epidemiological data for all cases of scarlet fever between 2004 and 2016 in China from the Chinese Public Health Science Data Center, the official website of National Health Commission of the People's Republic of China, and the National Notifiable Infectious Disease Surveillance System. These data had been collected from 31 provinces and regions in China and included geographical, seasonal, and patient demographic information. We used descriptive statistical methods and joinpoint regression to examine the spatiotemporal patterns and annual percentage change in incidence of the upsurge of disease across China.Findings: Between Jan 1, 2004, and Dec 31, 2016, 502 723 cases of scarlet fever, with ten fatalities, were reported in China, resulting in an annualised average incidence of 2·8807 per 100 000 people. The annual average incidence increased from 1·457 per 100 000 people in 2004 to 4·7638 per 100 000 people in 2011 (incidence rate ratio [IRR] 3·27, 95% CI 3·22-3·32; p<0·0001), peaking in 2015 (5·0092 per 100 000 people). The annual incidence after the 2011 upsurge of scarlet fever, between 2011 and 2016, was twice the average annual incidence reported between 2004 and 2010 (4·0125 vs 1·9105 per 100 000 people; IRR 2·07, 95% CI 2·06-2·09; p<0·0001). Most cases were distributed in the north, northeast, and northwest of the country. Semi-annual patterns were observed in May-June and November-December. The median age at onset of disease was 6 years, with the annual highest incidence observed in children aged 6 years (49·4675 per 100 000 people). The incidence among boys and men was 1·54 greater than that among girls and women before the upsurge, and 1·51 times greater after the upsurge (p<0·0001 for both). The median time from disease onset to reporting of the disease was shorter after the upsurge in disease than before (3 days vs 4 days; p=0·001).Interpretation: To our knowledge, this is the largest epidemiological study of scarlet fever worldwide. The patterns of infection across the country were similar before and after the 2011 upsurge, but the incidence of disease was substantially higher after 2011. Prevention and control strategies being implemented in response to this threat include improving disease surveillance and emergency response systems. In particular, the school absenteeism and symptom monitoring and early-warning system will contribute to the early diagnosis and report of the scarlet fever. This approach will help combat scarlet fever and other childhood infectious diseases in China.Funding: National Key R&D Plan of China Science and key epidemiological disciplines of Zhejiang Provincial Health of China. [ABSTRACT FROM AUTHOR]

The increasing dengue burden and epidemic severity worldwide have highlighted the need to improve surveillance. In non-endemic areas such as Taiwan, where outbreaks start mostly with imported cases from Southeast Asia, a closer examination of surveillance dynamics to detect cases early is necessary. To evaluate problems with dengue surveillance and investigate the involvement of different factors at various epidemic stages, we investigated 632 laboratory-confirmed indigenous dengue cases in Kaohsiung City, Taiwan during 2009–2010. The estimated sensitivity of clinical surveillance was 82.4% (521/632). Initially, the modified serological surveillance (targeting only the contacts of laboratory-confirmed dengue cases) identified clinically unrecognized afebrile cases in younger patients who visited private clinics and accounted for 30.4% (35/115) of the early-stage cases. Multivariate regression indicated that hospital/medical center visits [Adjusted Odds Ratio (aOR): 11.6, 95% confidence interval (CI): 6.3–21.4], middle epidemic stage [aOR: 2.4 (1.2–4.7)], fever [aOR: 2.3 (2.3–12.9)], and musculo-articular pain [aOR: 1.9 (1.05–3.3)] were significantly associated with clinical reporting. However, cases with pruritus/rash [aOR: 0.47 (0.26–0.83)] and diarrhea [aOR: 0.47 (0.26–0.85)] were underreported. In conclusion, multiple factors contributed to dengue surveillance problems. To prevent a large-scale epidemic and minimize severe dengue cases, there is a need for integrated surveillance incorporating entomological, clinical, serological, and virological surveillance systems to detect early cases, followed by immediate prevention and control measures and continuous evaluation to ensure effectiveness. This effort will be particularly important for an arbovirus, such as Zika virus, with a high asymptomatic infection ratio. For dengue- non-endemic countries, we recommend serological surveillance be implemented in areas with high Aedes mosquito indices or many breeding sites. Syndromic surveillance, spatial analysis and monitoring changes in epidemiological characteristics using a geographical information system, as well as epidemic prediction models involving epidemiological, meteorological and environmental variables will be helpful for early risk communication to increase awareness. [ABSTRACT FROM AUTHOR]

Background: Epidemics of hand, foot and mouth disease (HFMD) among children in East Asia have been a serious annual public health problem. Previous studies in China and island-type territories in East Asia showed that the onset of HFMD epidemics evolved with increased latitude. Based on the natural characteristics of the epidemics, we developed regression models for issuing aberration alerts and predictions.Methods: HFMD sentinel surveillance data from 2008 to 2014 in Japan are used in this study, covering 365 weeks and 47 prefectures between 24 and 46° of north latitude. Average HFMD cases per sentinel are standardized as Z rates. We fit weekly Z rate differences between prefectures located in the south and north of a designated prefecture with linear regression models to detect the surging trend of the epidemic for the prefecture. We propose a rule for issuing an aberration alert determined by the strength of the upward trend of south-north Z rate differences in the previous few weeks. In addition to the warning, we predict a Z rate for the next week with a 95% confidence interval.Results: We selected Tokyo and Kyoto for evaluating the proposed approach to aberration detection. Overall, the peaks of epidemics in Tokyo mostly occurred in weeks 28-31, later than in Kyoto, where the disease peaked in weeks 26-31. Positive south-north Z rate differences in both prefectures were clearly observed ahead of the HFMD epidemic cycles. Aberrations in the major epidemics of 2011 and 2013 were successfully detected weeks earlier. The prediction also provided accurate estimates of the epidemic's trends.Conclusions: We have used only the latitude, one geographical feature affecting the spatiotemporal distribution of HFMD, to develop rules for early aberration detection and prediction. We have also demonstrated that the proposed rules performed well using real data in terms of accuracy and timeliness. Although our approach may provide helpful information for controlling epidemics and minimizing the impact of diseases, the performance could be further improved by including other influential meteorological factors in the proposed latitude-based approach, which is worth further investigation. [ABSTRACT FROM AUTHOR]