Mapping genetic markers of artemisinin resistance in Plasmodium falciparum malaria in Asia: a systematic review and spatiotemporal analysis.

Background: The increase in artemisinin resistance threatens malaria elimination in Asia by the target date of 2030 and could derail control efforts in other endemic regions. This study aimed to develop up-to-date spatial distribution visualisations of the kelch13 (K13) gene markers of artemisinin resistance in Plasmodium falciparum for policy makers. Methods: In this systematic review and spatiotemporal analysis we used the WorldWide Antimalarial Resistance Network (WWARN) surveyor molecular markers of artemisinin resistance database. We updated the database by searching PubMed and SCOPUS for studies published between Jan 1, 1990, and March 31, 2021. Articles were included if they contained data on K13 markers of artemisinin resistance from patients' samples in Asia and articles already included in the WWARN database were excluded. Data were extracted from the published articles and authors were contacted when information was missing. We used the lowest administrative unit levels for the sampling locations of all the K13 data to describe the spatiotemporal distribution. The numbers of samples tested and those with each molecular marker in each administrative unit level were aggregated by year to calculate the marker prevalence over time. Findings: Data were collated from 72 studies comprising K13 markers from 16 613 blood samples collected from 1991 to 2020 from 18 countries. Most samples were from Myanmar (3842 [23·1%]), Cambodia (3804 [22·9%]), and Vietnam (2663 [16·0%]). The median time between data collection and publication was 3·6 years (range 0·9-25·0, IQR 2·7 [2·5-5·2]). There was a steady increase in the prevalence of WHO-validated K13 markers, with the lowest of 4·3% in 2005 (n=47) and the highest of 62·9% in 2018 (n=264). Overall, the prevalence of Cys580Tyr mutation increased from 48·9% in 2002 to 84·9% in 2018. Interpretation: From 2002 to 2018, there has been a steady increase in geographical locations and the proportion of infected people with validated artemisinin resistance markers. More consistent data collection, over more extended periods in the same areas with the rapid sharing of data are needed to map the spread and evolution of resistance to better inform policy decisions. Data in the literature are reported in a heterogeneous way leading to difficulties in pooling and interpretation. We propose here a tool with a set of minimum criteria for reporting future studies. Funding: This research was funded in part by the Wellcome Trust.

Bayesian spatio-temporal distributed lag modeling for delayed climatic effects on sparse malaria incidence data.

BACKGROUND: In many areas of the Greater Mekong Subregion (GMS), malaria endemic regions have shrunk to patches of predominantly low-transmission. With a regional goal of elimination by 2030, it is important to use appropriate methods to analyze and predict trends in incidence in these remaining transmission foci to inform planning efforts. Climatic variables have been associated with malaria incidence to varying degrees across the globe but the relationship is less clear in the GMS and standard methodologies may not be appropriate to account for the lag between climate and incidence and for locations with low numbers of cases. METHODS: In this study, a methodology was developed to estimate the spatio-temporal lag effect of climatic factors on malaria incidence in Thailand within a Bayesian framework. A simulation was conducted based on ground truth of lagged effect curves representing the delayed relation with sparse malaria cases as seen in our study population. A case study to estimate the delayed effect of environmental variables was used with malaria incidence at a fine geographic scale of sub-districts in a western province of Thailand. RESULTS: From the simulation study, the model assumptions which accommodated both delayed effects and excessive zeros appeared to have the best overall performance across evaluation metrics and scenarios. The case study demonstrated lagged climatic effect estimation of the proposed modeling with real data. The models appeared to be useful to estimate the shape of association with malaria incidence. CONCLUSIONS: A new method to estimate the spatiotemporal effect of climate on malaria trends in low transmission settings is presented. The developed methodology has potential to improve understanding and estimation of past and future trends in malaria incidence. With further development, this could assist policy makers with decisions on how to more effectively distribute resources and plan strategies for malaria elimination.

Low parasite connectivity among three malaria hotspots in Thailand.

Identifying sources and sinks of malaria transmission is critical for designing effective intervention strategies particularly as countries approach elimination. The number of malaria cases in Thailand decreased 90% between 2012 and 2020, yet elimination has remained a major public health challenge with persistent transmission foci and ongoing importation. There are three main hotspots of malaria transmission in Thailand: Ubon Ratchathani and Sisaket in the Northeast; Tak in the West; and Yala in the South. However, the degree to which these hotspots are connected via travel and importation has not been well characterized. Here, we develop a metapopulation model parameterized by mobile phone call detail record data to estimate parasite flow among these regions. We show that parasite connectivity among these regions was limited, and that each of these provinces independently drove the malaria transmission in nearby provinces. Overall, our results suggest that due to the low probability of domestic importation between the transmission hotspots, control and elimination strategies can be considered separately for each region.

Spatiotemporal distributed lag modelling of multiple Plasmodium species in a malaria elimination setting.

In much of the Greater Mekong Sub-region, malaria is now confined to patches and small foci of transmission. Malaria transmission is seasonal with the spatiotemporal patterns being associated with variation in environmental and climatic factors. However, the possible effect at different lag periods between meteorological variables and clinical malaria has not been well studied in the region. Thus, in this study we developed distributed lagged modelling accounting for spatiotemporal excessive zero cases in a malaria elimination setting. A multivariate framework was also extended to incorporate multiple data streams and investigate the spatiotemporal patterns from multiple parasite species via their lagged association with climatic variables. A simulation study was conducted to examine robustness of the methodology and a case study is provided of weekly data of clinical malaria cases at sub-district level in Thailand.

Incorporating human mobility data improves forecasts of Dengue fever in Thailand.

Over 390 million people worldwide are infected with dengue fever each year. In the absence of an effective vaccine for general use, national control programs must rely on hospital readiness and targeted vector control to prepare for epidemics, so accurate forecasting remains an important goal. Many dengue forecasting approaches have used environmental data linked to mosquito ecology to predict when epidemics will occur, but these have had mixed results. Conversely, human mobility, an important driver in the spatial spread of infection, is often ignored. Here we compare time-series forecasts of dengue fever in Thailand, integrating epidemiological data with mobility models generated from mobile phone data. We show that geographically-distant provinces strongly connected by human travel have more highly correlated dengue incidence than weakly connected provinces of the same distance, and that incorporating mobility data improves traditional time-series forecasting approaches. Notably, no single model or class of model always outperformed others. We propose an adaptive, mosaic forecasting approach for early warning systems.

Bayesian spatiotemporal modeling with sliding windows to correct reporting delays for real-time dengue surveillance in Thailand.

BACKGROUND: The ability to produce timely and accurate estimation of dengue cases can significantly impact disease control programs. A key challenge for dengue control in Thailand is the systematic delay in reporting at different levels in the surveillance system. Efficient and reliable surveillance and notification systems are vital to monitor health outcome trends and early detection of disease outbreaks which vary in space and time. METHODS: Predicting the trend in dengue cases in real-time is a challenging task in Thailand due to a combination of factors including reporting delays. We present decision support using a spatiotemporal nowcasting model which accounts for reporting delays in a Bayesian framework with sliding windows. A case study is presented to demonstrate the proposed nowcasting method using weekly dengue surveillance data in Bangkok at district level in 2010. RESULTS: The overall real-time estimation accuracy was 70.69% with 59.05% and 79.59% accuracy during low and high seasons averaged across all weeks and districts. The results suggest the model was able to give a reasonable estimate of the true numbers of cases in the presence of delayed reports in the surveillance system. With sliding windows, models could also produce similar accuracy to estimation with the whole data. CONCLUSIONS: A persistent challenge for the statistical and epidemiological communities is to transform data into evidence-based knowledge that facilitates policy making about health improvements and disease control at the individual and population levels. Improving real-time estimation of infectious disease incidence is an important technical development. The effort in this work provides a template for nowcasting in practice to inform decision making for dengue control.

Spatiotemporal epidemiology, environmental correlates, and demography of malaria in Tak Province, Thailand (2012-2015).

BACKGROUND: Tak Province, at the Thai-Myanmar border, is one of three high malaria incidence areas in Thailand. This study aimed to describe and identify possible factors driving the spatiotemporal trends of disease incidence from 2012 to 2015. METHODS: Climate variables and forest cover were correlated with malaria incidence using Pearson's r. Statistically significant clusters of high (hot spots) and low (cold spots) annual parasite incidence per 1000 population (API) were identified using Getis-Ord Gi* statistic. RESULTS: The total number of confirmed cases declined by 76% from 2012 to 2015 (Plasmodium falciparum by 81%, Plasmodium vivax by 73%). Incidence was highly seasonal with two main annual peaks. Most cases were male (62.75%), ≥ 15 years (56.07%), and of Myanmar (56.64%) or Thai (39.25%) nationality. Median temperature (1- and 2-month lags), average temperature (1- and 2-month lags) and average relative humidity (2- and 3-month lags) correlated positively with monthly total, P. falciparum and P. vivax API. Total rainfall in the same month correlated with API for total cases and P. vivax but not P. falciparum. At sub-district level, percentage forest cover had a low positive correlation with P. falciparum, P. vivax, and total API in most years. There was a decrease in API in most sub-districts for both P. falciparum and P. vivax. Sub-districts with the highest API were in the Tha Song Yang and Umphang Districts along the Thai-Myanmar border. Annual hot spots were mostly in the extreme north and south of the province. CONCLUSIONS: There has been a large decline in reported clinical malaria from 2012 to 2015 in Tak Province. API was correlated with monthly climate and annual forest cover but these did not account for the trends over time. Ongoing elimination interventions on one or both sides of the border are more likely to have been the cause but it was not possible to assess this due to a lack of suitable data. Two main hot spot areas were identified that could be targeted for intensified elimination activities.

An assessment of national surveillance systems for malaria elimination in the Asia Pacific.

BACKGROUND: Heads of Government from Asia and the Pacific have committed to a malaria-free region by 2030. In 2015, the total number of confirmed cases reported to the World Health Organization by 22 Asia Pacific countries was 2,461,025. However, this was likely a gross underestimate due in part to incidence data not being available from the wide variety of known sources. There is a recognized need for an accurate picture of malaria over time and space to support the goal of elimination. A survey was conducted to gain a deeper understanding of the collection of malaria incidence data for surveillance by National Malaria Control Programmes in 22 countries identified by the Asia Pacific Leaders Malaria Alliance. METHODS: In 2015-2016, a short questionnaire on malaria surveillance was distributed to 22 country National Malaria Control Programmes (NMCP) in the Asia Pacific. It collected country-specific information about the extent of inclusion of the range of possible sources of malaria incidence data and the role of the private sector in malaria treatment. The findings were used to produce recommendations for the regional heads of government on improving malaria surveillance to inform regional efforts towards malaria elimination. RESULTS: A survey response was received from all 22 target countries. Most of the malaria incidence data collected by NMCPs originated from government health facilities, while many did not collect comprehensive data from mobile and migrant populations, the private sector or the military. All data from village health workers were included by 10/20 countries and some by 5/20. Other sources of data included by some countries were plantations, police and other security forces, sentinel surveillance sites, research or academic institutions, private laboratories and other government ministries. Malaria was treated in private health facilities in 19/21 countries, while anti-malarials were available in private pharmacies in 16/21 and private shops in 6/21. Most countries use primarily paper-based reporting. CONCLUSIONS: Most collected malaria incidence data in the Asia Pacific is from government health facilities while data from a wide variety of other known sources are often not included in national surveillance databases. In particular, there needs to be a concerted regional effort to support inclusion of data on mobile and migrant populations and the private sector. There should also be an emphasis on electronic reporting and data harmonization across organizations. This will provide a more accurate and up to date picture of the true burden and distribution of malaria and will be of great assistance in helping realize the goal of malaria elimination in the Asia Pacific by 2030.

Use of a rapid test to assess plasma Plasmodium falciparum HRP2 and guide management of severe febrile illness.

BACKGROUND: Plasma Plasmodium falciparum histidine-rich protein-2 (PfHRP2) is the most accurate biomarker for severe malaria, but its measurement by ELISA has been considered too unwieldy to incorporate into clinical management. METHODS: Plasma samples covering a wide range of PfHRP2 concentrations were applied to rapid diagnostic tests (RDTs). RDTs were read by eye and digital capture, and PfHRP2 concentrations were measured via serial dilution with results compared to ELISA readings. RESULTS: The Paracheck (®) brand showed the strongest correlation (r(2) = 0.963) as well as the lowest inter-observer variability (combined kappa across band intensities for three observers = 0.938). Plasma PfHRP2 measurement via serial dilution showed minimal bias compared to ELISA and acceptable limits of agreement. Three different dilutions of a well characterized set of admission samples from uncomplicated and severe malaria patients studied in a low transmission setting gave an area under the receiver operator characteristic curve of 0.844 in terms of identifying severe malaria. CONCLUSIONS: These studies show that plasma PfHRP2 can be assessed via a single RDT, with application of a plasma dilution of 1:5 or 1:10 providing useful diagnostic information to assist in patient management or clinical trial inclusion.