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  1. Ng CFS, Seposo XT, Moi ML, Tajudin MABA, Madaniyazi L, Sahani M
    Int J Infect Dis, 2020 Dec;101:409-411.
    PMID: 33075527 DOI: 10.1016/j.ijid.2020.10.027
    The first wave of COVID-19 epidemic began in late January in Malaysia and ended with a very small final size. The second wave of infections broke out in late February and grew rapidly in the first 3 weeks. Authorities in the country responded quickly with a series of control strategies collectively known as the Movement Control Order (MCO) with different levels of intensity matching the progression of the epidemic. We examined the characteristics of the second wave and discussed the key control strategies implemented in the country. In the second wave, the epidemic doubled in size every 3.8 days (95% confidence interval [CI]: 3.3, 4.5) in the first month and decayed slowly after that with a halving time of approximately 3 weeks. The time-varying reproduction number Rt peaked at 3.1 (95% credible interval: 2.7, 3.5) in the 3rd week, declined sharply thereafter and stayed below 1 in the last 3 weeks of April, indicating low transmissibility approximately 3 weeks after the MCO. Experience of the country suggests that adaptive triggering of distancing policies combined with a population-wide movement control measure can be effective in suppressing transmission and preventing a rebound.
    Matched MeSH terms: Epidemics/statistics & numerical data
  2. Murray CJ, Ortblad KF, Guinovart C, Lim SS, Wolock TM, Roberts DA, et al.
    Lancet, 2014 Sep 13;384(9947):1005-70.
    PMID: 25059949 DOI: 10.1016/S0140-6736(14)60844-8
    BACKGROUND: The Millennium Declaration in 2000 brought special global attention to HIV, tuberculosis, and malaria through the formulation of Millennium Development Goal (MDG) 6. The Global Burden of Disease 2013 study provides a consistent and comprehensive approach to disease estimation for between 1990 and 2013, and an opportunity to assess whether accelerated progress has occured since the Millennium Declaration.

    METHODS: To estimate incidence and mortality for HIV, we used the UNAIDS Spectrum model appropriately modified based on a systematic review of available studies of mortality with and without antiretroviral therapy (ART). For concentrated epidemics, we calibrated Spectrum models to fit vital registration data corrected for misclassification of HIV deaths. In generalised epidemics, we minimised a loss function to select epidemic curves most consistent with prevalence data and demographic data for all-cause mortality. We analysed counterfactual scenarios for HIV to assess years of life saved through prevention of mother-to-child transmission (PMTCT) and ART. For tuberculosis, we analysed vital registration and verbal autopsy data to estimate mortality using cause of death ensemble modelling. We analysed data for corrected case-notifications, expert opinions on the case-detection rate, prevalence surveys, and estimated cause-specific mortality using Bayesian meta-regression to generate consistent trends in all parameters. We analysed malaria mortality and incidence using an updated cause of death database, a systematic analysis of verbal autopsy validation studies for malaria, and recent studies (2010-13) of incidence, drug resistance, and coverage of insecticide-treated bednets.

    FINDINGS: Globally in 2013, there were 1·8 million new HIV infections (95% uncertainty interval 1·7 million to 2·1 million), 29·2 million prevalent HIV cases (28·1 to 31·7), and 1·3 million HIV deaths (1·3 to 1·5). At the peak of the epidemic in 2005, HIV caused 1·7 million deaths (1·6 million to 1·9 million). Concentrated epidemics in Latin America and eastern Europe are substantially smaller than previously estimated. Through interventions including PMTCT and ART, 19·1 million life-years (16·6 million to 21·5 million) have been saved, 70·3% (65·4 to 76·1) in developing countries. From 2000 to 2011, the ratio of development assistance for health for HIV to years of life saved through intervention was US$4498 in developing countries. Including in HIV-positive individuals, all-form tuberculosis incidence was 7·5 million (7·4 million to 7·7 million), prevalence was 11·9 million (11·6 million to 12·2 million), and number of deaths was 1·4 million (1·3 million to 1·5 million) in 2013. In the same year and in only individuals who were HIV-negative, all-form tuberculosis incidence was 7·1 million (6·9 million to 7·3 million), prevalence was 11·2 million (10·8 million to 11·6 million), and number of deaths was 1·3 million (1·2 million to 1·4 million). Annualised rates of change (ARC) for incidence, prevalence, and death became negative after 2000. Tuberculosis in HIV-negative individuals disproportionately occurs in men and boys (versus women and girls); 64·0% of cases (63·6 to 64·3) and 64·7% of deaths (60·8 to 70·3). Globally, malaria cases and deaths grew rapidly from 1990 reaching a peak of 232 million cases (143 million to 387 million) in 2003 and 1·2 million deaths (1·1 million to 1·4 million) in 2004. Since 2004, child deaths from malaria in sub-Saharan Africa have decreased by 31·5% (15·7 to 44·1). Outside of Africa, malaria mortality has been steadily decreasing since 1990.

    INTERPRETATION: Our estimates of the number of people living with HIV are 18·7% smaller than UNAIDS's estimates in 2012. The number of people living with malaria is larger than estimated by WHO. The number of people living with HIV, tuberculosis, or malaria have all decreased since 2000. At the global level, upward trends for malaria and HIV deaths have been reversed and declines in tuberculosis deaths have accelerated. 101 countries (74 of which are developing) still have increasing HIV incidence. Substantial progress since the Millennium Declaration is an encouraging sign of the effect of global action.

    FUNDING: Bill & Melinda Gates Foundation.

    Matched MeSH terms: Epidemics/statistics & numerical data
  3. Cauchemez S, Epperson S, Biggerstaff M, Swerdlow D, Finelli L, Ferguson NM
    PLoS Med, 2013;10(3):e1001399.
    PMID: 23472057 DOI: 10.1371/journal.pmed.1001399
    BACKGROUND: Prior to emergence in human populations, zoonoses such as SARS cause occasional infections in human populations exposed to reservoir species. The risk of widespread epidemics in humans can be assessed by monitoring the reproduction number R (average number of persons infected by a human case). However, until now, estimating R required detailed outbreak investigations of human clusters, for which resources and expertise are not always available. Additionally, existing methods do not correct for important selection and under-ascertainment biases. Here, we present simple estimation methods that overcome many of these limitations.

    METHODS AND FINDINGS: Our approach is based on a parsimonious mathematical model of disease transmission and only requires data collected through routine surveillance and standard case investigations. We apply it to assess the transmissibility of swine-origin influenza A H3N2v-M virus in the US, Nipah virus in Malaysia and Bangladesh, and also present a non-zoonotic example (cholera in the Dominican Republic). Estimation is based on two simple summary statistics, the proportion infected by the natural reservoir among detected cases (G) and among the subset of the first detected cases in each cluster (F). If detection of a case does not affect detection of other cases from the same cluster, we find that R can be estimated by 1-G; otherwise R can be estimated by 1-F when the case detection rate is low. In more general cases, bounds on R can still be derived.

    CONCLUSIONS: We have developed a simple approach with limited data requirements that enables robust assessment of the risks posed by emerging zoonoses. We illustrate this by deriving transmissibility estimates for the H3N2v-M virus, an important step in evaluating the possible pandemic threat posed by this virus. Please see later in the article for the Editors' Summary.

    Matched MeSH terms: Epidemics/statistics & numerical data*
  4. Ochani RK, Batra S, Shaikh A, Asad A
    Infez Med, 2019 Jun 01;27(2):117-127.
    PMID: 31205033
    The Nipah virus was discovered twenty years ago, and there is considerable information available regarding the specificities surrounding this virus such as transmission, pathogenesis and genome. Belonging to the Henipavirus genus, this virus can cause fever, encephalitis and respiratory disorders. The first cases were reported in Malaysia and Singapore in 1998, when affected individuals presented with severe febrile encephalitis. Since then, much has been identified about this virus. These single-stranded RNA viruses gain entry into target cells via a process known as macropinocytosis. The viral genome is released into the cell cytoplasm via a cascade of processes that involves conformational changes in G and F proteins which allow for attachment of the viral membrane to the cell membrane. In addition to this, the natural reservoirs of this virus have been identified to be fruit bats from the genus Pteropus. Five of the 14 species of bats in Malaysia have been identified as carriers, and this virus affects horses, cats, dogs, pigs and humans. Various mechanisms of transmission have been proposed such as contamination of date palm saps by bat feces and saliva, nosocomial and human-to-human transmissions. Physical contact was identified as the strongest risk factor for developing an infection in the 2004 Faridpur outbreak. Geographically, the virus seems to favor the Indian sub-continent, Indonesia, Southeast Asia, Pakistan, southern China, northern Australia and the Philippines, as demonstrated by the multiple outbreaks in 2001, 2004, 2007, 2012 in Bangladesh, India and Pakistan as well as the initial outbreaks in Malaysia and Singapore. Multiple routes of the viremic spread in the human body have been identified such as the central nervous system (CNS) and respiratory system, while virus levels in the body remain low, detection in the cerebrospinal fluid is comparatively high. The virus follows an incubation period of 4 days to 2 weeks which is followed by the development of symptoms. The primary clinical signs include fever, headache, vomiting and dizziness, while the characteristic symptoms consist of segmental myoclonus, tachycardia, areflexia, hypotonia, abnormal pupillary reflexes and hypertension. The serum neutralization test (SNT) is the gold standard of diagnosis followed by ELISA if SNT cannot be carried out. On the other hand, treatment is supportive since there a lack of effective pharmacological therapy and only one equine vaccine is currently licensed for use. Prevention of outbreaks seems to be a more viable approach until specific therapeutic strategies are devised.
    Matched MeSH terms: Epidemics/statistics & numerical data*
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