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Background The public health impact of the coronavirus disease 2019 (COVID-19) pandemic has motivated a rapid search for potential therapeutics, with some key successes. However, the potential impact of different treatments, and consequently research and procurement priorities, have not been clear. Methods Using a mathematical model of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission, COVID-19 disease and clinical care, we explore the public-health impact of different potential therapeutics, under a range of scenarios varying healthcare capacity, epidemic trajectories; and drug efficacy in the absence of supportive care. Results The impact of drugs like dexamethasone (delivered to the most critically-ill in hospital and whose therapeutic benefit is expected to depend on the availability of supportive care such as oxygen and mechanical ventilation) is likely to be limited in settings where healthcare capacity is lowest or where uncontrolled epidemics result in hospitals being overwhelmed. As such, it may avert 22% of deaths in high-income countries but only 8% in low-income countries (assuming R = 1.35). Therapeutics for different patient populations (those not in hospital, early in the course of infection) and types of benefit (reducing disease severity or infectiousness, preventing hospitalization) could have much greater benefits, particularly in resource-poor settings facing large epidemics. Conclusions Advances in the treatment of COVID-19 to date have been focused on hospitalized-patients and predicated on an assumption of adequate access to supportive care. Therapeutics delivered earlier in the course of infection that reduce the need for healthcare or reduce infectiousness could have significant impact, and research into their efficacy and means of delivery should be a priority.

BackgroundThe unprecedented public health impact of the COVID-19 pandemic has motivated a rapid search for potential therapeutics, with some key successes. However, the potential impact of different treatments, and consequently research and procurement priorities, have not been clear.Methods and FindingsWe develop a mathematical model of SARS-CoV-2 transmission, COVID-19 disease and clinical care to explore the potential public-health impact of a range of different potential therapeutics, under a range of different scenarios varying: i) healthcare capacity, ii) epidemic trajectories; and iii) drug efficacy in the absence of supportive care. In each case, the outcome of interest was the number of COVID-19 deaths averted in scenarios with the therapeutic compared to scenarios without. We find the impact of drugs like dexamethasone (which are delivered to the most critically-ill in hospital and whose therapeutic benefit is expected to depend on the availability of supportive care such as oxygen and mechanical ventilation) is likely to be limited in settings where healthcare capacity is lowest or where uncontrolled epidemics result in hospitals being overwhelmed. As such, it may avert 22% of deaths in high-income countries but only 8% in low-income countries (assuming R=1.35). Therapeutics for different patient populations (those not in hospital, early in the course of infection) and types of benefit (reducing disease severity or infectiousness, preventing hospitalisation) could have much greater benefits, particularly in resource-poor settings facing large epidemics.ConclusionsThere is a global asymmetry in who is likely to benefit from advances in the treatment of COVID-19 to date, which have been focussed on hospitalised-patients and predicated on an assumption of adequate access to supportive care. Therapeutics that can feasibly be delivered to those earlier in the course of infection that reduce the need for healthcare or reduce infectiousness could have significant impact, and research into their efficacy and means of delivery should be a priority.

Summary Background The ability to accurately predict early progression of dengue to severe disease is crucial for patient triage and clinical management. Previous systematic reviews and meta-analyses have found significant heterogeneity in predictors of severe disease due to large variation in these factors during the time course of the illness. We aimed to identify factors associated with progression to severe dengue disease that are detectable specifically in the febrile phase. Methods We did a systematic review and meta-analysis to identify predictors identifiable during the febrile phase associated with progression to severe disease defined according to WHO criteria. Eight medical databases were searched for studies published from Jan 1, 1997, to Jan 31, 2020. Original clinical studies in English assessing the association of factors detected during the febrile phase with progression to severe dengue were selected and assessed by three reviewers, with discrepancies resolved by consensus. Meta-analyses were done using random-effects models to estimate pooled effect sizes. Only predictors reported in at least four studies were included in the meta-analyses. Heterogeneity was assessed using the Cochrane Q and I2 statistics, and publication bias was assessed by Egger's test. We did subgroup analyses of studies with children and adults. The study is registered with PROSPERO, CRD42018093363. Findings Of 6643 studies identified, 150 articles were included in the systematic review, and 122 articles comprising 25 potential predictors were included in the meta-analyses. Female patients had a higher risk of severe dengue than male patients in the main analysis (2674 [16·2%] of 16 481 vs 3052 [10·5%] of 29 142; odds ratio [OR] 1·13 [95% CI 1·01–1·26) but not in the subgroup analysis of studies with children. Pre-existing comorbidities associated with severe disease were diabetes (135 [31·3%] of 431 with vs 868 [16·0%] of 5421 without; crude OR 4·38 [2·58–7·43]), hypertension (240 [35·0%] of 685 vs 763 [20·6%] of 3695; 2·19 [1·36–3·53]), renal disease (44 [45·8%] of 96 vs 271 [16·0%] of 1690; 4·67 [2·21–9·88]), and cardiovascular disease (nine [23·1%] of 39 vs 155 [8·6%] of 1793; 2·79 [1·04–7·50]). Clinical features during the febrile phase associated with progression to severe disease were vomiting (329 [13·5%] of 2432 with vs 258 [6·8%] of 3797 without; 2·25 [1·87–2·71]), abdominal pain and tenderness (321 [17·7%] of 1814 vs 435 [8·1%] of 5357; 1·92 [1·35–2·74]), spontaneous or mucosal bleeding (147 [17·9%] of 822 vs 676 [10·8%] of 6235; 1·57 [1·13–2·19]), and the presence of clinical fluid accumulation (40 [42·1%] of 95 vs 212 [14·9%] of 1425; 4·61 [2·29–9·26]). During the first 4 days of illness, platelet count was lower (standardised mean difference −0·34 [95% CI −0·54 to −0·15]), serum albumin was lower (−0·5 [–0·86 to −0·15]), and aminotransferase concentrations were higher (aspartate aminotransferase [AST] 1·06 [0·54 to 1·57] and alanine aminotransferase [ALT] 0·73 [0·36 to 1·09]) among individuals who progressed to severe disease. Dengue virus serotype 2 was associated with severe disease in children. Secondary infections (vs primary infections) were also associated with severe disease (1682 [11·8%] of 14 252 with vs 507 [5·2%] of 9660 without; OR 2·26 [95% CI 1·65–3·09]). Although the included studies had a moderate to high risk of bias in terms of study confounding, the risk of bias was low to moderate in other domains. Heterogeneity of the pooled results varied from low to high on different factors. Interpretation This analysis supports monitoring of the warning signs described in the 2009 WHO guidelines on dengue. In addition, testing for infecting serotype and monitoring platelet count and serum albumin, AST, and ALT concentrations during the febrile phase of illness could improve the early prediction of severe dengue. Funding Wellcome Trust, National Institute for Health Research, Collaborative Project to Increase Production of Rural Doctors, and Royal Thai Government.

Additional file 2: Figure S1. Correlation matrix of the eight variables in the data set (see Additional file 2: Table S1 for the variable definitions). The size and colour represent the correlation between any two variables, with larger circles representing higher correlation. Figure S2. Age distribution of the samples collected in the 2009 and 2013 serosurveys separately, by cluster. Age was classified as belonging to one of the following age-groups: 16–20; 21–25; 26–30; 31–35; 36–40; 41–45; 46–50; 51–55; 56–60 years. Figure S3. Median (bold line), interquantiles (box), range (vertical line) and outliers (points) of the log10 PRNT50 titres against DENV1-4 characterising the five clusters obtained with the agglomerative hierarchical clustering algorithm applied to the 2009 dataset. The colours blue, grey, green and brown represent DENV1, DENV2, DENV3 and DENV4, respectively. Figure S4. Median (bold line), interquantiles (box), range (vertical line) and outliers (points) of the log10 PRNT50 titres against DENV1-4 characterising the six clusters obtained with the agglomerative hierarchical clustering algorithm applied to the 2013 dataset. The colours blue, grey, green and brown represent DENV1, DENV2, DENV3 and DENV4, respectively.

Without an effective vaccine or specific treatment for dengue, the ability to accurately predict early progression to severe disease relies on patient triage and clinical management. Previous systematic reviews and meta-analyses have found significant heterogeneity in predictors associated with severe disease, due to large variations of these factors during the time-course of the illness. Our study aimed to identify the factors associated with progression to severe dengue disease, which are detectable specifically in the febrile phase. We conducted a systematic review and meta-analysis to identify predictors associated with disease progression identifiable during the febrile phase. Eight medical databases were searched for studies published from January 1997 to February 2018. Relevant studies were selected and assessed by three reviewers with discrepancies resolved by consensus. Meta-analyses were performed using random-effects models to estimate pooled effect sizes. Heterogeneity and publication bias were also assessed. Of 5,293 studies identified, 98 were included in the meta-analyses. The presence of pre-existing comorbidities (diabetes mellitus, hypertension and renal disease) were associated with severe dengue. Vomiting, abdominal pain and tenderness, spontaneous and mucosal bleeding, and clinical fluid accumulation were clinical features associated with progression to severe disease. During the first four days of illness, platelet count, and serum albumin were lower among individuals who progressed to severe disease whilst aminotransferase levels were higher. Dengue virus serotype 2 and secondary infection were also associated with severe disease. This analysis supports the monitoring of the warning signs described in the 2009 WHO guidelines. In addition, testing for infecting serotype and monitoring platelet count, serum albumin, AST and ALT during the febrile phase of illness would improve the early prediction of severe dengue. Funding Statement: The authors state: "There was no funding source for this study." SS acknowledges funding from the Collaborative Project to Increase Production of Rural Doctor and Royal Thai Government Scholarship. ID ackowledges research funding from an Imperial College Junior Research Fellowship, a Wellcome Trust Sir Henry Dale Fellowship, Janssen Pharmaceutica and joint Centre funding from the UK Medical Research Council and Department for International Development. AH is a National Institute for Health Research (NIHR) Senior Investigator. She also acknowledges the support of the NIHR Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at Imperial College London in partnership with Public Health England (PHE), in collaboration with The Sanger Institute, the University of Cambridge Veterinary School and Imperial College Health Partners Declaration of Interests: The authors declare no competing interests. Ethics Approval Statement: We followed the protocol described in the Preferred Reporting Items for Systematic Reviews and Meta-analyses for Protocols (PRISMA-P) guidelines and the steps discussed in the guide to systematic review and meta-analysis of prognostic factor studies by Riley et al . Our protocol was registered with PROSPERO (CRD42018093363).

The World Health Organization (WHO) developed the ASSURED criteria to describe the ideal characteristics for point-of-care testing in low-resource settings: affordable, sensitive, specific, user-friendly, rapid and robust, equipment-free and deliverable.1 These standards describe. Over the last decade, widespread adoption of point-of-care testing has led to significant changes in clinical decision-making processes. The development of compact molecular diagnostics, such as the GeneXpert® platform, have enabled short turnaround times and allowed profiling of antimicrobial resistance. Although modern assays have increased operational requirements, many devices are robust and can be operated within communities with minimal training. These new generation of rapid tests have bypassed barriers to care and enabled treatment to take place independently from central facilities. Here we describe the importance of connectivity, the automatic capture and sharing of patient healthcare data from testing, in the adoption and roll-out of rapid testing.