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In this paper we report a quantitative laser Biospeckle method using VDRL plates to monitor the activity of Trypanosoma cruzi and the calibration conditions including three image processing algorithms and three programs (ImageJ and two programs designed in this work). Benznidazole was used as a test drug. Variable volume (constant density) and variable density (constant volume) were used for the quantitative evaluation of parasite activity in calibrated wells of the VDRL plate. The desiccation process within the well was monitored as a function of volume and of the activity of the Biospeckle pattern of the parasites as well as the quantitative effect of the surface parasite quantity (proportion of the object’s plane). A statistical analysis was performed with ANOVA, Tukey post hoc and Descriptive Statistics using R and R Commander. Conditions of volume (100μl) and parasite density (2-4x104 parasites/well, in exponential growth phase), assay time (up to 204min), frame number (11 frames), algorithm and program (RCommander/SAGA) for image processing were selected to test the effect of variable concentrations of benznidazole (0.0195 to 20μg/mL / 0.075 to 76.8μM) at various times (1, 61, 128 and 204min) on the activity of the Biospeckle pattern. The flat wells of the VDRL plate were found to be suitable for the quantitative calibration of the activity of Trypanosoma cruzi using the appropriate algorithm and program. Under these conditions, benznidazole produces at 1min an instantaneous effect on the activity of the Biospeckle pattern of T. cruzi, which remains with a similar profile up to 1 hour. A second effect which is dependent on concentrations above 1.25μg/mL and is statistically different from the effect at lower concentrations causes a decrease in the activity of the Biospeckle pattern. This effect is better detected after 1 hour of drug action. This behavior may be explained by an instantaneous effect on a membrane protein of Trypanosoma cruzi that could mediate the translocation of benznidazole. At longer times the effect may possibly be explained by the required transformation of the pro-drug into the active drug. [ABSTRACT FROM AUTHOR]

Background: Algorithms to diagnose gambiense human African trypanosomiasis (HAT, sleeping sickness) are often complex due to the unsatisfactory sensitivity and/or specificity of available tests, and typically include a screening (serological), confirmation (parasitological) and staging component. There is insufficient evidence on the relative accuracy of these algorithms. This paper presents estimates of the accuracy of five algorithms used by past Médecins Sans Frontières programmes in the Republic of Congo, Southern Sudan and Uganda. Methodology and Principal Findings: The sequence of tests in each algorithm was programmed into a probabilistic model, informed by distributions of the sensitivity, specificity and staging accuracy of each test, constructed based on a literature review. The accuracy of algorithms was estimated in a baseline scenario and in a worst-case scenario introducing various near worst-case assumptions. In the baseline scenario, sensitivity was estimated as 85–90% in all but one algorithm, with specificity above 99.9% except for the Republic of Congo, where CATT serology was used as independent confirmation test: here, positive predictive value (PPV) was estimated at <50% in realistic active screening prevalence scenarios. Furthermore, most algorithms misclassified about one third of true stage 1 cases as stage 2, and about 10% of true stage 2 cases as stage 1. In the worst-case scenario, sensitivity was 75–90% and PPV no more than 75% at 1% prevalence, with about half of stage 1 cases misclassified as stage 2. Conclusions: Published evidence on the accuracy of widely used tests is scanty. Algorithms should carefully weigh the use of serology alone for confirmation, and could enhance sensitivity through serological suspect follow-up and repeat parasitology. Better evidence on the frequency of low-parasitaemia infections is needed. Simulation studies should guide the tailoring of algorithms to specific scenarios of HAT prevalence and availability of control tools. Author Summary: Gambiense human African trypanosomiasis (HAT, sleeping sickness) usually features low prevalence. The two stages of the disease require different treatments, and stage 2 is fatal if untreated. HAT diagnosis must therefore be highly sensitive (i.e., detect as many true cases as possible) and specific (i.e., minimize false positives). HAT diagnostic algorithms are complex and involve several tests to screen for, confirm and stage infection. We analyzed five algorithms used by Médecins Sans Frontières HAT programmes. We combined published data on the accuracy of each test in the algorithm with a computer program that simulates all possible algorithm branches. We found that all algorithms had reasonable sensitivity (85–90%); specificity was high (>99.9%) except for the Republic of Congo, where confirmation did not rely on microscopic evidence, resulting in frequent false positives (but also higher sensitivity). Algorithms misclassified about one third of stage 1 cases as stage 2, but stage 2 classification was highly accurate. The use of serology alone for confirmation merits caution. HAT diagnosis could be made more sensitively by following up serological suspects and repeating microscopic examinations. Computer simulations can help to adapt algorithms to local conditions in each HAT programme, such as the prevalence of infection and operational constraints. [ABSTRACT FROM AUTHOR]

Endemic to the Democratic Republic of the Congo (DRC), monkeypox is a zoonotic disease that causes smallpox-like illness in humans. Observed fluctuations in reported cases over time raises questions about when it is appropriate to mount a public health response, and what specific actions should be taken. We evaluated three different thresholds to differentiate between baseline and heightened disease incidence, and propose a novel, tiered algorithm for public health action. Monkeypox surveillance data from Tshuapa Province, 2011–2013, were used to calculate three different statistical thresholds: Cullen, c-sum, and a World Health Organization (WHO) method based on monthly incidence. When the observed cases exceeded the threshold for a given month, that month was considered to be ‘aberrant’. For each approach, the number of aberrant months detected was summed by year—each method produced vastly different results. The Cullen approach generated a number of aberrant signals over the period of consideration (9/36 months). The c-sum method was the most sensitive (30/36 months), followed by the WHO method (12/24 months). We conclude that triggering public health action based on signals detected by a single method may be inefficient and overly simplistic for monkeypox. We propose instead a response algorithm that integrates an objective threshold (WHO method) with contextual information about epidemiological and spatiotemporal links between suspected cases to determine whether a response should be operating under i) routine surveillance ii) alert status, or iii) outbreak status. This framework could be modified and adopted by national and zone level health workers in monkeypox-endemic countries. Lastly, we discuss considerations for selecting thresholds for monkeypox outbreaks across gradients of endemicity and public health resources. [ABSTRACT FROM AUTHOR]

Background: This study aimed to investigate the spatiotemporal clustering and socio-environmental factors associated with dengue fever (DF) incidence rates at street level in Guangzhou City, China. Methods: Spatiotemporal scan technique was applied to identify the high risk region of DF. Multiple regression model was used to identify the socio-environmental factors associated with DF infection. A Poisson regression model was employed to examine the spatiotemporal patterns in the spread of DF. Results: Spatial clusters of DF were primarily concentrated at the southwest part of Guangzhou City. Age group (65+ years) (Odd Ratio (OR) = 1.49, 95% Confidence Interval = 1.13 to 2.03), floating population (OR = 1.09, 95% CI = 1.05 to 1.15), low-education (OR = 1.08, 95% CI = 1.01 to 1.16) and non-agriculture (OR = 1.07, 95% CI = 1.03 to 1.11) were associated with DF transmission. Poisson regression results indicated that changes in DF incidence rates were significantly associated with longitude (β = -5.08, P<0.01) and latitude (β = -1.99, P<0.01). Conclusions: The study demonstrated that social-environmental factors may play an important role in DF transmission in Guangzhou. As geographic range of notified DF has significantly expanded over recent years, an early warning systems based on spatiotemporal model with socio-environmental is urgently needed to improve the effectiveness and efficiency of dengue control and prevention. [ABSTRACT FROM AUTHOR]

Background: Chagas disease is due to the parasite Trypanosoma cruzi, a protist disseminated by a Triatome vector. This disease is endemic to Latin America and considered by WHO as one of the 17 world’s neglected diseases. In Europe and in North America, imported cases are also detected, due to migration of population outside of the endemic region. Diagnosis of T. cruzi infection is usually made indirectly by the detection of specific antibodies to T. cruzi antigens. Following initial diagnostic evaluation or screening test (qualifying or discarding blood donation), a confirmation test is performed for samples initially reactive. The test presented in this study aims at the confirmation/refutation of the infectious status of human blood samples and will permit taking appropriate clinical measures. Methodology/Principal Findings: We designed a novel array of twelve antigens and printed these antigens onto 96-well plates. We tested 248 positive samples T. cruzi, 94 unscreened blood donors’ samples from non-endemic area, 49 seronegative blood donors, 7 false-positive and 3 doubtful samples. The observed reactivities were analyzed to propose a decision-tree algorithm that correctly classifies all the samples, with the potential to discriminate false-positive results and sticky samples. We observed that antibodies levels (Sum of all antigens) was significantly higher for PCR positive than for PCR negative samples in all studied groups with Multi-cruzi. Conclusion/Significance: The results described in this study indicate that the Multi-cruzi improves the serological confirmation of Chagas disease. Moreover the “sum of all antigens” detected by Multi-cruzi could reflect parasitemia level in patients–like PCR signals does—and could serve as an indicator of parasite clearance in longitudinal follow-ups. Validation of this assay is still required on an independent large collection of well characterized samples including typical false-reactive samples such as Leishmaniasis. [ABSTRACT FROM AUTHOR]

Background: In Taiwan, around 1,500 cases of dengue fever are reported annually and incidence has been increasing over time. A national web-based Notifiable Diseases Surveillance System (NDSS) has been in operation since 1997 to monitor incidence and trends and support case and outbreak management. We present the findings of an evaluation of the NDSS to ascertain the extent to which dengue fever surveillance objectives are being achieved. Methodology: We extracted the NDSS data on all laboratory-confirmed dengue fever cases reported during 1 January 2010 to 31 December 2012 to assess and describe key system attributes based on the Centers for Disease Control and Prevention surveillance evaluation guidelines. The system’s structure and processes were delineated and operational staff interviewed using a semi-structured questionnaire. Crude and age-adjusted incidence rates were calculated and key demographic variables were summarised to describe reporting activity. Data completeness and validity were described across several variables. Principal Findings: Of 5,072 laboratory-confirmed dengue fever cases reported during 2010–2012, 4,740 (93%) were reported during July to December. The system was judged to be simple due to its minimal reporting steps. Data collected on key variables were correctly formatted and usable in > 90% of cases, demonstrating good data completeness and validity. The information collected was considered relevant by users with high acceptability. Adherence to guidelines for 24-hour reporting was 99%. Of 720 cases (14%) recorded as travel-related, 111 (15%) had an onset >14 days after return, highlighting the potential for misclassification. Information on hospitalization was missing for 22% of cases. The calculated PVP was 43%. Conclusions/Significance: The NDSS for dengue fever surveillance is a robust, well maintained and acceptable system that supports the collection of complete and valid data needed to achieve the surveillance objectives. The simplicity of the system engenders compliance leading to timely and accurate reporting. Completeness of hospitalization information could be further improved to allow assessment of severity of illness. To minimize misclassification, an algorithm to accurately classify travel cases should be established. [ABSTRACT FROM AUTHOR]

Background: Dengue and malaria are two major public health concerns in tropical settings. Although the pathogeneses of these two arthropod-borne diseases differ, their clinical and biological presentations are unspecific. During dengue epidemics, several hundred patients with fever and diffuse pain are weekly admitted at the emergency room. It is difficult to discriminate them from patients presenting malaria attacks. Furthermore, it may be impossible to provide a parasitological microscopic examination for all patients. This study aimed to establish a diagnostic algorithm for communities where dengue fever and malaria occur at some frequency in adults. Methodology/Principal Findings: A sub-study using the control groups of a case-control study in French Guiana – originally designed to compare dengue and malaria co-infected cases to single infected cases – was performed between 2004 and 2010. In brief, 208 patients with malaria matched to 208 patients with dengue fever were compared in the present study. A predictive score of malaria versus dengue was established using .632 bootstrap procedures. Multivariate analysis showed that male gender, age, tachycardia, anemia, thrombocytopenia, and CRP>5 mg/l were independently associated with malaria. The predictive score using those variables had an AUC of 0.86 (95%CI: 0.82–0.89), and the CRP was the preponderant predictive factor. The sensitivity and specificity of CRP>5 mg/L to discriminate malaria from dengue were of 0.995 (95%CI: 0.991–1) and 0.35 (95%CI 0.32–0.39), respectively. Conclusions/Significance: The clinical and biological score performed relatively well for discriminating cases of dengue versus malaria. Moreover, using only the CRP level turned to be a useful biomarker to discriminate feverish patients at low risk of malaria in an area where both infections exist. It would avoid more than 33% of unnecessary parasitological examinations with a very low risk of missing a malaria attack. [ABSTRACT FROM AUTHOR]