Software Reliability Modeling Incorporating Fault Detection and Fault Correction Processes with Testing Coverage and Fault Amount Dependency

This paper presents a general testing coverage software reliability modeling framework that covers imperfect debugging and considers not only fault detection processes (FDP) but also fault correction processes (FCP). Numerous software reliability growth models have evaluated the reliability of software over the last few decades, but most of them attached importance to modeling the fault detection process rather than modeling the fault correction process. Previous studies analyzed the time dependency between the fault detection and correction processes and modeled the fault correction process as a delayed detection process with a random or deterministic time delay. We study the quantitative dependency between dual processes from the viewpoint of fault amount dependency instead of time dependency, then propose a generalized modeling framework along with imperfect debugging and testing coverage. New models are derived by adopting different testing coverage functions. We compared the performance of these proposed models with existing models under the context of two kinds of failure data, one of which only includes observations of faults detected, and the other includes not only fault detection but also fault correction data. Different parameter estimation methods and performance comparison criteria are presented according to the characteristics of different kinds of datasets. No matter what kind of data, the comparison results reveal that the proposed models generally give improved descriptive and predictive performance than existing models.

Estimating the Local Burden of Disease During the First Wave of the COVID-19 Epidemic in England, Using Different Data Sources from Changing Surveillance Practices

Background The COVID-19 epidemic has differentially impacted communities across England, with regional variation in rates of confirmed cases, hospitalisations and deaths. Measurement of this burden changed substantially over the first months, as surveillance was expanded to accommodate the escalating epidemic. Laboratory confirmation was initially restricted to clinical need (“pillar 1”) before expanding to community-wide symptomatics (“pillar 2”). This study aimed to ascertain whether inconsistent measurement of case data resulting from varying testing coverage could be reconciled by drawing inference from COVID-19-related deaths. MethodsWe fit a Bayesian spatio-temporal model to weekly COVID-19-related deaths per local authority (LTLA) throughout the first wave (1 January - 30 June 2020), adjusting for the local epidemic timing and the age, deprivation and ethnic composition of its population. We combined predictions from this model with case data under community-wide, symptomatic testing and infection prevalence estimates from the ONS infection survey, to infer the likely trajectory of infections implied by the deaths in each LTLA.ResultsA model including temporally- and spatially-correlated random effects was found to best accommodate the observed variation in COVID-19-related deaths, after accounting for local population characteristics. Predicted case counts under community-wide symptomatic testing suggest a total of 275,000-420,000 cases over the first wave - a median of over 100,000 additional to the total confirmed in practice under varying testing coverage. This translates to a peak incidence of around 200,000 total infections per week across England. The extent to which estimated total infections are reflected in confirmed case counts was found to vary substantially across LTLAs, ranging from 7% in Leicester to 96% in Gloucester with a median of 23%. ConclusionsLimitations in testing capacity biased the observed trajectory of COVID-19 infections throughout the first wave. Basing inference on COVID-19-related mortality and higher-coverage testing later in the time period, we could explore the extent of this bias more explicitly. Evidence points towards substantial under-representation of initial growth and peak magnitude of infections nationally, to which different parts of the country contribute unequally.

Management and containment of a SARS-CoV-2 Beta variant outbreak at the Malawi University of Science and Technology

Outbreaks of COVID at university campuses can spread rapidly and threaten the broader community. We describe the management of an outbreak at a Malawian university in April 2021 during Malawi's second wave. Classes were suspended following detection of infections by routine testing and campus-wide PCR mass testing was conducted. Fifty seven cases were recorded, 55 among students, two among staff. Classes resumed 28 days after suspension following two weeks without cases. Just 6.3% of full-time staff and 87.4% of outsourced staff tested while 65% of students at the main campus and 74% at the extension campus were tested. Final year students had significantly higher positivity and lower testing coverage compared to freshmen. All viruses sequenced were beta variant and at least four separate virus introductions onto campus were observed. These findings are useful for development of campus outbreak responses and indicate the need to emphasize staff, males and senior students in testing.

Software reliability prediction and release time management with coverage

PurposeSoftware testing is needed to produce extremely reliable software products. A crucial decision problem that the software developer encounters is to ascertain when to terminate the testing process and when to release the software system in the market. With the growing need to deliver quality software, the critical assessment of reliability, cost of testing and release time strategy is requisite for project managers. This study seeks to examine the reliability of the software system by proposing a generalized testing coverage-based software reliability growth model (SRGM) that incorporates the effect of testing efforts and change point. Moreover, the strategic software time-to-market policy based on costreliability criteria is suggested.Design/methodology/approachThe fault detection process is modeled as a composite function of testing coverage, testing efforts and the continuation time of the testing process. Also, to assimilate factual scenarios, the current research exhibits the influence of software users refer as reporters in the fault detection process. Thus, this study models the reliability growth phenomenon by integrating the number of reporters and the number of instructions executed in the field environment. Besides, it is presumed that the managers release the software early to capture maximum market share and continue the testing process for an added period in the user environment. The multiattribute utility theory (MAUT) is applied to solve the optimization model with release time and testing termination time as two decision variables.FindingsThe practical applicability and performance of the proposed methodology are demonstrated through real-life software failure data. The findings of the empirical analysis have shown the superiority of the present study as compared to conventional approaches.Originality/valueThis study is the first attempt to assimilate testing coverage phenomenon in joint optimization of software time to market and testing duration.

Integrated PCR testing and extended window contact tracing system for COVID-19 to improve comprehensiveness and speed

Testing and contact tracing are standard tools for controlling the spread of COVID-191. Their effectiveness hinges on a sequence of processes encompassing testing coverage and timeliness, testing quality and speed of reporting, contact tracing speed and comprehensiveness and compliance with advice given2–6. We optimized this sequence of processes in the context of a public health program targeting around 33,000 higher education students through a combination of low barrier PCR testing with rapid turn-around-time, close integration of testing and tracing teams and IT infrastructure, community engagement and the implementation of bidirectional contact tracing by extending the contact tracing window from 2 to 7 days before symptom onset or test of the index case. We anticipate this combined intervention to help improve epidemic control.

Tuberculosis contact investigation following the stone-in-the-pond principle in the Netherlands – Did adjusted guidelines improve efficiency?

Background In low tuberculosis (TB) incidence countries, contact investigation (CI) requires not missing contacts with TB infection or disease without unnecessarily evaluating non-infected contacts. Aim We assessed whether updated guidelines for the stone-in-the-pond principle and their promotion improved CI practices. Methods This retrospective study used surveillance data to compare CI outcomes before (2011–2013) and after (2014–2016) the guideline update and promotion. Using negative binomial regression and logistic regression models, we compared the number of contacts invited for CI per index patient, the number of CI scaled-up according to the stone-in-the-pond principle, the TB and latent TB infection (LTBI) testing coverage, and yield. Results Pre and post update, 1,703 and 1,489 index patients were reported, 27,187 and 21,056 contacts were eligible for CI, 86% and 89% were tested for TB, and 0.70% and 0.73% were identified with active TB, respectively. Post update, the number of casual contacts invited per index patient decreased statistically significantly (RR = 0.88; 95% CI: 0.79–0.98), TB testing coverage increased (OR = 1.4; 95% CI: 1.2–1.7), and TB yield increased (OR = 2.0; 95% CI: 1.0–3.9). The total LTBI yield increased from 8.8% to 9.8%, with statistically significant increases for casual (OR = 1.2; 95% CI: 1.0–1.5) and community contacts (OR = 2.0; 95% CI: 1.6–3.2). The proportion of CIs appropriately scaled-up to community contacts increased statistically significantly (RR = 1.8; 95% CI: 1.3–2.6). Conclusion This study shows that promoting evidence-based CI guidelines strengthen the efficiency of CIs without jeopardising effectiveness. These findings support CI is an effective TB elimination intervention.

Integrated PCR testing and extended window contact tracing system for COVID-19 to improve comprehensiveness and speed

Testing and contact tracing are standard tools for controlling the spread of COVID-191. Their effectiveness hinges on a sequence of processes encompassing testing coverage and timeliness, testing quality and speed of reporting, contact tracing speed and comprehensiveness and compliance with advice given2–6. We optimized this sequence of processes in the context of a public health program targeting around 33,000 higher education students through a combination of low barrier PCR testing with rapid turn-around-time, close integration of testing and tracing teams and IT infrastructure, community engagement and the implementation of bidirectional contact tracing by extending the contact tracing window from 2 to 7 days before symptom onset or test of the index case. We anticipate this combined intervention to help improve epidemic control.

A self-testing kits distribution intervention improved HIV testing of men who have sex with men and their sexual partners: a 12-month stepped-wedge randomized controlled trial in China (Preprint)

BACKGROUND Men who have sex with men (MSM) have high HIV incidence and prevalence burdens but relatively low HIV testing rates. Literature showed insufficient evidence on the efficacy of Social media (WeChat) based HIV self-testing (HIVST) kits distribution approaches among MSM and their sexual partners. OBJECTIVE This study evaluated an Social media (WeChat) based HIVST distribution intervention in increasing HIV testing uptake among MSM and their sexual partners in China. METHODS The study used a 12-month stepped-wedge randomized controlled trial design and an online survey mode. MSM who were HIV-negative or with unknown HIV status were recruited through social media marketing and outreaches by community opinion leaders between August and December 2018 in Shenyang, Beijing, Chongqing, and Shenzhen. Participants were randomly allocated to four groups, which were intervened by sequence. The intervention group received free HIVST kits on top of the control condition that participants received standard education and care. Participants were followed up from January to December 2019. Generalized linear models were used to assess HIV testing coverage and frequency difference between the intervention and the control stage. RESULTS Each group enrolled 140 eligible MSM with a total of 560 participants. Twelve participants were diagnosed with HIV infections, and 4 of them were in the follow-up period. Participants in the intervention stage were nine times likely to receive an HIV test than in the control stage (85.6% vs. 39.2%, risk ratio [RR]=9.21, 95%CI 5.92~14.33). The intervention also increased participants’ HIV testing frequency (1.48 vs. 0.59 times, risk difference [RD]=0.89, 95%CI 0.82~0.96). Moreover, the intervention increased HIV testing proportion (49.0% vs. 30.1%, RR=2.23, 95%CI 1.46~3.40) and mean HIV testing frequency (0.69 vs. 0.35 times, RD=0.26, 95%CI 0.15~0.38) among participants sexual partners who received HIVST through secondary distribution. CONCLUSIONS An Social media (WeChat) based HIVST distribution intervention effectively increased HIV testing coverage and frequency in MSM and their sexual partners. Future programs could apply this approach in HIV education and testing efforts to reduce HIV incidence. CLINICALTRIAL This study was registered at the Chinese Clinical Trials website (http://www.chictr.org.cn/index.aspx) with the registration tracking number of ChiCTR1800019453 on November 12, 2018. INTERNATIONAL REGISTERED REPORT RR2-10.2196/17788