Simulating a Transmission Assessment Survey: An evaluation of current methods used in determining the elimination of the neglected tropical disease, Lymphatic Filariasis

The World Health Organization (WHO) recommends Transmission Assessment Surveys (TAS) to determine when an evaluation unit (EU) (a designated population survey area) has achieved elimination of transmission of the vector-borne macroparasitic disease Lymphatic Filariasis (LF). These determinations are based on combining data from multiple survey units within an EU; it is unclear how underlying cluster-level variation influences the outcome of the TAS at EU level. We simulate LF infection distribution in an EU and compare three methods for assessing whether LF elimination has occurred based on currently recommended decision thresholds and sampling methods.

We simulate an EU divided into clusters of varying size and disease prevalence. We produce 1000 samples according to LF TAS examples and WHO guidelines and compare three decision-making approaches: lot quality assurance sampling (LQAS) (recommended by WHO), one-sided interval estimate (CI), and n^th order statistic (MAX). Summary statistics demonstrating the "pass" rate for the EU under different disease transmission conditions are generated using a versatile SAS® macro.

As the prevalence of LF decreases, the LQAS and CI approaches produce increased likelihood of a pass outcome for an EU while some cluster units may still have a high likelihood of transmission. The MAX provides an alternative that increases the likelihood of determining a pass only once the whole area has a low likelihood of transmission. LQAS and CI approaches designed to estimate the LF prevalence in the EU miss hotspots that will continue to transmit infection while the MAX approach focuses on identifying clusters with high risk of transmission.

The current TAS methodology has a flaw that may result in false predictions of LF transmission interruption throughout an EU. Modifying the TAS methodology to address results from extreme clusters rather than being based on mean prevalence over an EU will result in greater success for global elimination of LF.