Epidemic surveillance in a low resource setting: lessons from an evaluation of the Solomon Islands syndromic surveillance system, 2017

Citizen science as a tool for arboviral vector surveillance in a resourced-constrained setting: results of a pilot study in Honiara, Solomon Islands, 2019

Background

Recent arboviral disease outbreaks highlight the value a better understanding of the spread of disease-carrying mosquitoes across spatial-temporal scales can provide. Traditional surveillance tools are limited by jurisdictional boundaries, workforce constraints, logistics, and cost; factors that in low- and middle-income countries often conspire to undermine public health protection efforts. To overcome these, we undertake a pilot study designed to explore if citizen science provides a feasible strategy for arboviral vector surveillance in small developing Pacific island contexts.

Methods

We recruited, trained, and equipped community volunteers to trap and type mosquitos within their household settings, and to report count data to a central authority by short-message-service. Mosquito catches were independently assessed to measure participants’ mosquito identification accuracy. Other data were collected to measure the frequency and stability of reporting, and volunteers’ experiences.

Results

Participants collected data for 78.3% of the study period, and agreement between the volunteer citizen scientists’ and the reviewing entomologist’s mosquito identification was 94%. Opportunity to contribute to a project of social benefit, the chance to learn new skills, and the frequency of engagement with project staff were prime motivators for participation. Unstable electricity supply (required to run the trap’s fan), insufficient personal finances (to buy electricity and phone credit), and inconvenience were identified as barriers to sustained participation.

Conclusions

While there are challenges to address, our findings suggest that citizen science offers an opportunity to overcome the human resource constraints that conspire to limit health authorities’ capacity to monitor arboviral vectors across populations. We note that the success of citizen science-based surveillance is dependent on the appropriate selection of equipment and participants, and the quality of engagement and support provided.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12889-021-10493-6.

The comparative performance of wavelet-based outbreak detector, exponential weighted moving average, and Poisson regression-based methods in detection of pertussis outbreaks in Iranian infants: A simulation-based study

BACKGROUND

Early detection of outbreaks of transmissible diseases is essential for public health. This study aimed to determine the performance of the wavelet-based outbreak detection method (WOD) in detecting outbreaks and to compare its performance with the Poisson regression-based model and exponentially weighted moving average (EWMA) using data of simulated pertussis outbreaks in Iran.

METHOD

The data on suspected cases of pertussis from 25th February 2012 to 23rd March 2018 in Iran was used. The performance of the WOD (Daubechies 10 [db10] and Haar wavelets), Poisson regression-based method, and EWMA Compared in terms of timeliness and detection of outbreak days using the simulation of different outbreaks. In the current study, two simulations were used, one based on retrospectively collected data (literature-based) on pertussis cases and another one on a synthetic dataset created by the researchers. The sensitivity, specificity, false alarm, and false-negative rate, positive and negative likelihood ratios, under receiver operating characteristics areas, and median timeliness were used to assess the performance of the methods.

RESULTS

In a literature-based outbreak simulation, the highest and lowest sensitivity, false negative in the detection of injected outbreaks were seen in db10, with sensitivity 0.59 (0.56-0.62), and Haar wavelets with 0.57 (0.54-0.60). In the researcher simulated data, the EWMA (K = 0.5) with sensitivity 0.92 (0.90-0.94) had the best performance. About timeliness, the WOD methods showed the best performance in the early warning of the outbreak in both simulation approaches.

CONCLUSION

Performance of the WOD in the early alarming outbreaks was appropriate. However, this method would be best used along with other methods of public health surveillance.