New advances in the treatment of hepatitis C provide high levels of sustained viral response but their expense limits availability in publicly funded health systems. The aim of this review was to estimate the proportion of patients who will spontaneously clear HCV, to identify factors that are associated with clearance and to support better targeting of directly acting antivirals. We searched Ovid EMBASE, Ovid MEDLINE and PubMed from 1 January 1994 to 30 June 2015 for studies reporting hepatitis C spontaneous clearance and/or demographic, clinical and behavioural factors associated with clearance. We undertook meta-analyses to estimate the odds of clearance for each predictor. Forty-three studies met the inclusion criteria, representing 20 110 individuals, and 6 of these studies included sufficient data to estimate spontaneous clearance. The proportion achieving clearance within 3, 6, 12 and 24 months following infection were, respectively, 19.8% (95% CI: 2.6%-47.5%), 27.9% (95% CI: 17.2%-41.8%), 36.1% (95% CI: 23.5%-50.9%) and 37.1% (95% CI: 23.7%-52.8%). Individuals who had not spontaneously cleared by 12 months were unlikely to do so. The likelihood of spontaneous clearance was lower in males and individuals with HIV co-infection, the absence of HBV co-infection, asymptomatic infection, black or nonindigenous race, nongenotype 1 infection, older age and alcohol or drug problems. This study suggests that patients continue to spontaneously clear HCV for at least 12 months following initial infection. However, injecting drug users are comparatively less likely to achieve clearance; thus, they should be considered a priority for early treatment given the continuing risks that these individuals pose for onwards transmission.

The exponential accumulation, processing and accrual of big data in healthcare are only possible through an equally rapidly evolving field of big data analytics. The latter offers the capacity to rationalize, understand and use big data to serve many different purposes, from improved services modelling to prediction of treatment outcomes, to greater patient and disease stratification. In the area of infectious diseases, the application of big data analytics has introduced a number of changes in the information accumulation models. These are discussed by comparing the traditional and new models of data accumulation. Big data analytics is fast becoming a crucial component for the modelling of transmission-aiding infection control measures and policies-emergency response analyses required during local or international outbreaks. However, the application of big data analytics in infectious diseases is coupled with a number of ethical impacts. Four key areas are discussed in this paper: (i) automation and algorithmic reliance impacting freedom of choice, (ii) big data analytics complexity impacting informed consent, (iii) reliance on profiling impacting individual and group identities and justice/fair access and (iv) increased surveillance and population intervention capabilities impacting behavioural norms and practices. Furthermore, the extension of big data analytics to include information derived from personal devices, such as mobile phones and wearables as part of infectious disease frameworks in the near future and their potential ethical impacts are discussed. Considered together, Philos. Technol. DOI 10.1007/s13347-017-0278-y Chiara Garattini and Jade Raffle contributed equally to this work. Profiscio Ltd., 10 Great Russell Street, London WC1B 3BQ, UK the need for a constructive and transparent inclusion of ethical questioning in this rapidly evolving field becomes an increasing necessity in order to provide a moral foundation for the societal acceptance and responsible development of the technological advancement.

Background Since the first cases reported in Wuhan, China, in December 2019, the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has spread worldwide. In Indonesia, the first case was reported in early March 2020, and the numbers of confirmed infections have been increasing until now. Efforts to contain the virus globally and in Indonesia are ongoing. This is the very first manuscript using a spatial-temporal model to describe the SARS-CoV-2 transmission in Indonesia, as well as providing a patient profile for all confirmed COVID-19 cases. Method Data was collected from the official website of the Indonesia National Task Force for the Acceleration of COVID-19, from the period of 02 March 2020–02 August 2020. The data from RT-PCR confirmed, SARS-CoV-2 positive patients was categorized according to demographics, symptoms and comorbidities based on case categorization (confirmed, recovered, dead). The data collected provides granular and thorough information on time and geographical location for all 34 Provinces across Indonesia. Results A cumulative total of 111,450 confirmed cases of were reported in Indonesia during the study period. Of those confirmed cases 67.79% (75,551/111,450) were shown as recovered and 4.83% (5,382/111,450) of them as died. Patients were mostly male (50.52%; 56,300/111,450) and adults aged 31 to 45 years old (29.73%; 33,132/111,450). Overall patient presentation symptoms of cough and fever, as well as chronic disease comorbidities were in line with previously published data from elsewhere in South-East Asia. The data reported here, shows that from the detection of the first confirmed case and within a short time period of 40 days, all the provinces of Indonesia were affected by COVID-19. Conclusions This study is the first to provide detailed characteristics of the confirmed SARS-CoV-2 patients in Indonesia, including their demographic profile and COVID-19 presentation history. It used a spatial-temporal analysis to present the epidemic spread from the very beginning of the outbreak throughout all provinces in the country. The increase of new confirmed cases has been consistent during this time period for all provinces, with some demonstrating a sharp increase, in part due to the surge in national diagnostic capacity. This information delivers a ready resource that can be used for prediction modelling, and is utilized continuously by the current Indonesian Task Force in order to advise on potential implementation or removal of public distancing measures, and on potential availability of healthcare capacity in their efforts to ultimately manage the outbreak.

BackgroundIndonesia has had more recorded human cases of influenza A H5N1 than any other country, with one of the world’s highest case fatality rates. Understanding barriers to treatment may help ensure life-saving influenza-specific treatment is provided early enough to meaningfully improve clinical outcomes.MethodsData for this observational study of humans infected with influenza A H5N1 were obtained primarily from Ministry of Health, Provincial and District Health Office clinical records. Data included time from symptom onset to presentation for medical care, source of medical care provided, influenza virology, time to initiation of influenza-specific treatment with antiviral drugs, and survival.ResultsData on 124 human cases of virologically confirmed avian influenza were collected between September 2005 and December 2010, representing 73% of all reported Indonesia cases. The median time from health service presentation to antiviral drug initiation was 7.0 days. Time to viral testing was highly correlated with starting antiviral treatment (p < 0.0001). We found substantial variability in the time to viral testing (p = 0.04) by type of medical care provider. Antivirals were started promptly after diagnosis (median 0 days).ConclusionsDelays in the delivery of appropriate care to human cases of avian influenza H5N1 in Indonesia appear related to delays in diagnosis rather than presentation to health care settings. Either cases are not suspected of being H5N1 cases until nearly one week after presenting for medical care, or viral testing and/or antiviral treatment is not available where patients are presenting for care. Health system delays have increased since 2007.